Research articles for the 2021-01-08
Anticorruption and Corporate Investment in China: Evidence from a Quasi-Natural Experiment
SSRN
We evaluate the impact of the last Chinese anti-corruption campaign (ACC) on firms’ investment. We exploit the sudden and abnormal surge in the rate of “Tigers†investigations due to the increase in anti-corruption monitoring. This change led to an overall decrease in investment of private firms, but not state-owned ones. The impact was stronger for the inefficient companies and curbed the overinvestment rates of the most efficient ones. Results are robust to additional tests and do not depend on the political uncertainty introduced by the ACC. We conclude that the ACC modified the opportunity costs of investing in China.
SSRN
We evaluate the impact of the last Chinese anti-corruption campaign (ACC) on firms’ investment. We exploit the sudden and abnormal surge in the rate of “Tigers†investigations due to the increase in anti-corruption monitoring. This change led to an overall decrease in investment of private firms, but not state-owned ones. The impact was stronger for the inefficient companies and curbed the overinvestment rates of the most efficient ones. Results are robust to additional tests and do not depend on the political uncertainty introduced by the ACC. We conclude that the ACC modified the opportunity costs of investing in China.
Buy Together, but Recycle Alone: Sentiment-Driven Herding Behavior in Oceanic Dry Bulk Shipping
SSRN
We employ the vessels that comprise the dry bulk segment of the maritime industry and examine how market sentiment affects the herding behavior of shipping investors in a real asset market. Our results show that the behavioral aspect of investing, measured through intentional and unintentional herding, contrary to the results for financial markets, is affected by sentiment on the buy side (newbuildings) but not on the sell side (scrapping). Furthermore, we provide evidence that when market sentiment is negative, investors tend to follow market leaders (intentional herding), while, when sentiment is positive, unintentional herding leads to common investment practices among shipping investors. Our results have significant implications both for academics and for practitioners since they reflect a clear distinction of the pattern of investment decisions for real assets, compared to financial assets.
SSRN
We employ the vessels that comprise the dry bulk segment of the maritime industry and examine how market sentiment affects the herding behavior of shipping investors in a real asset market. Our results show that the behavioral aspect of investing, measured through intentional and unintentional herding, contrary to the results for financial markets, is affected by sentiment on the buy side (newbuildings) but not on the sell side (scrapping). Furthermore, we provide evidence that when market sentiment is negative, investors tend to follow market leaders (intentional herding), while, when sentiment is positive, unintentional herding leads to common investment practices among shipping investors. Our results have significant implications both for academics and for practitioners since they reflect a clear distinction of the pattern of investment decisions for real assets, compared to financial assets.
Can the Premium for Idiosyncratic Tail Risk be Explained by Exposures to its Common Factor?
SSRN
Stocks in the highest idiosyncratic tail risk decile earn 8% higher average annualized returns than in the lowest. I propose a risk-based explanation for this premium, in which shocks to intermediary funding cause idiosyncratic tail risk to follow a strong factor structure, and the factor, common idiosyncratic tail risk (CITR), comoves with intermediary funding. Consequently, if firms with high idiosyncratic tail risk have high exposure to CITR shocks, then they earn a risk premium due to their low returns when intermediary constraints tighten. To test my explanation, I create a novel measure of idiosyncratic tail risk that is estimated using high-frequency returns, and theoretically establish its time-aggregation properties. Consistent with my explanation, CITR shocks are procyclical, are correlated to intermediary factors, are priced in assets, and explain the idiosyncratic tail risk premium. Furthermore, volume tail risk also earns a premium, follows a strong factor structure, and its common factor is priced. This duality of idiosyncratic tail risk and volume tail risk provides evidence for my risk-based explanation, and further supports the hypothesis that intermediaries' large trades cause idiosyncratic tail risk and volume tail risk from Gabaix et al. (2006).
SSRN
Stocks in the highest idiosyncratic tail risk decile earn 8% higher average annualized returns than in the lowest. I propose a risk-based explanation for this premium, in which shocks to intermediary funding cause idiosyncratic tail risk to follow a strong factor structure, and the factor, common idiosyncratic tail risk (CITR), comoves with intermediary funding. Consequently, if firms with high idiosyncratic tail risk have high exposure to CITR shocks, then they earn a risk premium due to their low returns when intermediary constraints tighten. To test my explanation, I create a novel measure of idiosyncratic tail risk that is estimated using high-frequency returns, and theoretically establish its time-aggregation properties. Consistent with my explanation, CITR shocks are procyclical, are correlated to intermediary factors, are priced in assets, and explain the idiosyncratic tail risk premium. Furthermore, volume tail risk also earns a premium, follows a strong factor structure, and its common factor is priced. This duality of idiosyncratic tail risk and volume tail risk provides evidence for my risk-based explanation, and further supports the hypothesis that intermediaries' large trades cause idiosyncratic tail risk and volume tail risk from Gabaix et al. (2006).
Clustering Default Syndromes Across Italian SMEs
SSRN
The quantitative analyses related to firms’ default prediction extensively analyzed which balance sheet ratios include significant information on the probability of default of a firm. These analyses are typically aimed at measuring a generic default risk, while no analyses are aimed at describing the syndromes affecting firms and bringing them to default. Defining syndromes, which means describing how and why firms default, can be of fundamental importance for evaluating which specific weakness a firm is affected from, and which specific intervention can fix the problem. In this paper, we analyzed a panel of Italian small and medium sized companies (SMEs) for verifying if some syndromes can be found. Results shows that the main syndromes affecting the Italian SMEs are quite similar over sectors, even if each sector is characterized by specific equilibriums and values.
SSRN
The quantitative analyses related to firms’ default prediction extensively analyzed which balance sheet ratios include significant information on the probability of default of a firm. These analyses are typically aimed at measuring a generic default risk, while no analyses are aimed at describing the syndromes affecting firms and bringing them to default. Defining syndromes, which means describing how and why firms default, can be of fundamental importance for evaluating which specific weakness a firm is affected from, and which specific intervention can fix the problem. In this paper, we analyzed a panel of Italian small and medium sized companies (SMEs) for verifying if some syndromes can be found. Results shows that the main syndromes affecting the Italian SMEs are quite similar over sectors, even if each sector is characterized by specific equilibriums and values.
Cutting Operational Costs by Integrating Fintech into Traditional Banking Firms
SSRN
Fintech firms mobilize information technology to provide intermediation services using a broker methodology, whereas dealer banks intermediate using leveraged balance sheets. The integration of Fintech into banking may reduce the unit cost of intermediation by shifting the production function from dealer to broker. Identifying commonalities in the financial structures of Fintech-adopting banks, we develop the "Fintech score." Analysis of on-balance sheet lending, securitization, brokered deposits and non-interest income demonstrates that more broker-like (dealer-like) banks have high (low) Fintech scores. Using Data Envelopment and Stochastic Cost Frontier Analyses, banks with higher Fintech scores are more operationally efficient and resilient in crises.
SSRN
Fintech firms mobilize information technology to provide intermediation services using a broker methodology, whereas dealer banks intermediate using leveraged balance sheets. The integration of Fintech into banking may reduce the unit cost of intermediation by shifting the production function from dealer to broker. Identifying commonalities in the financial structures of Fintech-adopting banks, we develop the "Fintech score." Analysis of on-balance sheet lending, securitization, brokered deposits and non-interest income demonstrates that more broker-like (dealer-like) banks have high (low) Fintech scores. Using Data Envelopment and Stochastic Cost Frontier Analyses, banks with higher Fintech scores are more operationally efficient and resilient in crises.
Discreteâ€"Time Optimal Execution Under a Generalized Price Impact Model With Markovian Exogenous Orders
SSRN
This paper examines a discrete-time optimal trade execution problem with generalized price impact. We extend a model recently discussed, which considers price impacts of aggregate random trade orders posed by small traders as well as a large trader. In contrast that assumes aggregate trading volumes submitted by small traders are serially independent, this paper allows a Markovian dependence.Our new problem is formulated as a Markov decision process with state variables including the last small traders' aggregate orders. Over a finite horizon, the large trader with Constant Absolute Risk Aversion (CARA) von Neumann-Morgenstern (vN-M) utility function maximizes the expected utility from the final wealth. By applying the backward induction method of dynamic programming, we characterize the optimal value function and optimal trade execution strategy, and conclude that the execution strategy is a time-dependent affine function of three state variables. Moreover, numerical analysis prevails that the optimal execution strategy admits a `statistical arbitrage' via a round-trip trading, although our model considers a linear permanent price impact, which does not admit any price manipulation or arbitrage. The reason is that our model considers price impacts caused by small traders' orders with a Markovian dependence.
SSRN
This paper examines a discrete-time optimal trade execution problem with generalized price impact. We extend a model recently discussed, which considers price impacts of aggregate random trade orders posed by small traders as well as a large trader. In contrast that assumes aggregate trading volumes submitted by small traders are serially independent, this paper allows a Markovian dependence.Our new problem is formulated as a Markov decision process with state variables including the last small traders' aggregate orders. Over a finite horizon, the large trader with Constant Absolute Risk Aversion (CARA) von Neumann-Morgenstern (vN-M) utility function maximizes the expected utility from the final wealth. By applying the backward induction method of dynamic programming, we characterize the optimal value function and optimal trade execution strategy, and conclude that the execution strategy is a time-dependent affine function of three state variables. Moreover, numerical analysis prevails that the optimal execution strategy admits a `statistical arbitrage' via a round-trip trading, although our model considers a linear permanent price impact, which does not admit any price manipulation or arbitrage. The reason is that our model considers price impacts caused by small traders' orders with a Markovian dependence.
Diversifying Macroeconomic Factors â€" For Better or for Worse
SSRN
It is widely acknowledged that asset returns are driven by common sources of risk, especially in challenging times when the benefits from traditional portfolio diversification fail to realize. From a top-down perspective, investors are mostly concerned about shocks in growth or inflation that ultimately govern the pricing of broad asset classes. To this extent, we propose a natural asset allocation framework to achieve a diversified exposure to orthogonal macro risk factors and to harvest the associated long-term premia. We examine the role and usefulness of different types of macroeconomic variables, as systematic sources of risk or state variables that drive time variation in the asset returns, and compare their diversification potential across different states of the world.
SSRN
It is widely acknowledged that asset returns are driven by common sources of risk, especially in challenging times when the benefits from traditional portfolio diversification fail to realize. From a top-down perspective, investors are mostly concerned about shocks in growth or inflation that ultimately govern the pricing of broad asset classes. To this extent, we propose a natural asset allocation framework to achieve a diversified exposure to orthogonal macro risk factors and to harvest the associated long-term premia. We examine the role and usefulness of different types of macroeconomic variables, as systematic sources of risk or state variables that drive time variation in the asset returns, and compare their diversification potential across different states of the world.
Do Directorship Returns Affect CEO Turnover?
SSRN
Boards staffed by directors who have experienced high stock returns at other firms where they hold directorships are more likely to fire their CEOs. A one-standard-deviation increase in such directorship returns increases the probability of forced turnover by 11-19%. Directorship returns have a larger impact when they are more recent and when CEO ability is harder or costlier to evaluate, consistent with boards using an availability heuristic to form beliefs about the ability gap between incumbent and replacement CEOs. Conditional on forced turnover, directorship returns are inversely correlated with subsequent improvements in operating performance, suggesting that such beliefs are suboptimal.
SSRN
Boards staffed by directors who have experienced high stock returns at other firms where they hold directorships are more likely to fire their CEOs. A one-standard-deviation increase in such directorship returns increases the probability of forced turnover by 11-19%. Directorship returns have a larger impact when they are more recent and when CEO ability is harder or costlier to evaluate, consistent with boards using an availability heuristic to form beliefs about the ability gap between incumbent and replacement CEOs. Conditional on forced turnover, directorship returns are inversely correlated with subsequent improvements in operating performance, suggesting that such beliefs are suboptimal.
Does Quantitative Easing Mitigate the Sovereign-Bank Nexus?
SSRN
The credit risk of the sovereign affects the financial health of its banking sector and vice versa, creating an adverse feedback loop known as “sovereign- bank nexusâ€. We show that Quantitative Easing can effectively mitigate the sovereign-bank nexus. Our results indicate that the ECB’s Public Sector Purchase Programme reduced the co-movement of sovereign and bank credit risk by almost 80%. The mitigation is driven by the euro area periphery and works through three channels: (i) a reduction in government bond holdings of banks, (ii) an increase of government bond prices, and (iii) an increase in excess liquidity holdings of banks.
SSRN
The credit risk of the sovereign affects the financial health of its banking sector and vice versa, creating an adverse feedback loop known as “sovereign- bank nexusâ€. We show that Quantitative Easing can effectively mitigate the sovereign-bank nexus. Our results indicate that the ECB’s Public Sector Purchase Programme reduced the co-movement of sovereign and bank credit risk by almost 80%. The mitigation is driven by the euro area periphery and works through three channels: (i) a reduction in government bond holdings of banks, (ii) an increase of government bond prices, and (iii) an increase in excess liquidity holdings of banks.
Fat Tails Arise Endogenously in Asset Prices From Supply/Demand, With or Without Jump Processes
SSRN
Fat tails arise endogenously from modeling of price change based on a quotient of arbitrarily correlated demand and supply (i.e., excess demand) whether or not jump discontinuities are present. The assumption is that supply and demand are described by drift terms, Brownian (i.e., Gaussians or normals) and compound Poisson jump processes. If P^( âˆ'1)dP/dt (the relative price change in an interval dt) is given by a suitable function of excess demand, D/Sâˆ'1 (where D and S are demand and supply), then the distribution has tail behavior F (x) ∼ x^(âˆ'ζ )for a power ζ that depends on the function G in P ^(âˆ'1)dP/dt = G (D/S). For G (x) ∼ |x|1/q one has ζ = q. The value, q ∈ [3, 5] is in agreement with empirical data. While many theoretical explanations have been offered for the paradox of fat tails, we show that this issue never arises if one models price dynamics using basic economics methodology, rather than the usual starting point for classical finance which assumes a normal distribution of price changes. The function, G, can be calibrated in the absence of rare events. The results establish a simple link between the decay exponent of the density function and the price adjustment function, a feature that can improve methodology for risk assessment.
SSRN
Fat tails arise endogenously from modeling of price change based on a quotient of arbitrarily correlated demand and supply (i.e., excess demand) whether or not jump discontinuities are present. The assumption is that supply and demand are described by drift terms, Brownian (i.e., Gaussians or normals) and compound Poisson jump processes. If P^( âˆ'1)dP/dt (the relative price change in an interval dt) is given by a suitable function of excess demand, D/Sâˆ'1 (where D and S are demand and supply), then the distribution has tail behavior F (x) ∼ x^(âˆ'ζ )for a power ζ that depends on the function G in P ^(âˆ'1)dP/dt = G (D/S). For G (x) ∼ |x|1/q one has ζ = q. The value, q ∈ [3, 5] is in agreement with empirical data. While many theoretical explanations have been offered for the paradox of fat tails, we show that this issue never arises if one models price dynamics using basic economics methodology, rather than the usual starting point for classical finance which assumes a normal distribution of price changes. The function, G, can be calibrated in the absence of rare events. The results establish a simple link between the decay exponent of the density function and the price adjustment function, a feature that can improve methodology for risk assessment.
FinTech Marketplace Lending, Default Risk, and the Business Cycle
SSRN
This paper explores default in the FinTech marketplace lending market using a dataset of both extensive credit and soft information for borrowers from the largest marketplace lender in the United States. I find that both macro and regional economic conditions play a role in consumer default and should be taken into consideration when assessing credit risk. I show that lenders operating in this market increasingly focus on subprime borrowers, whose default rates are more sensitive to macro and regional economic conditions than those of prime borrowers. Based on estimates from a duration model, I provide counterfactual analyses of what default rates and the associated total losses would look like in different economic scenarios. In the case of a recession, the losses would be 37 percent higher than in the case of an expansion. For the same volume of loans in the recession, doubling the subprime share would lead to an additional 7.5 percent increase in losses.
SSRN
This paper explores default in the FinTech marketplace lending market using a dataset of both extensive credit and soft information for borrowers from the largest marketplace lender in the United States. I find that both macro and regional economic conditions play a role in consumer default and should be taken into consideration when assessing credit risk. I show that lenders operating in this market increasingly focus on subprime borrowers, whose default rates are more sensitive to macro and regional economic conditions than those of prime borrowers. Based on estimates from a duration model, I provide counterfactual analyses of what default rates and the associated total losses would look like in different economic scenarios. In the case of a recession, the losses would be 37 percent higher than in the case of an expansion. For the same volume of loans in the recession, doubling the subprime share would lead to an additional 7.5 percent increase in losses.
Fintech: What’s Old, What’s New?
SSRN
We study the effects of technological change on financial intermediation, distinguishing between innovations in information (data collection and processing) and communication (relationships and distribution). Both follow historical trends towards an increased use of hard information and less in-person interaction, which are accelerating rapidly. We evaluate more recent innovations, such as the combination of data abundance and artificial intelligence, and the rise of digital platforms. We argue that the rise of new communication channels can lead to the vertical and horizontal disintegration of the traditional bank business model. Specialized providers of financial services can chip away activities that do not rely on access to balance sheets, while platforms can interject themselves between banks and customers. We discuss limitations to these challenges to the traditional bank business model, and the resulting policy implications.
SSRN
We study the effects of technological change on financial intermediation, distinguishing between innovations in information (data collection and processing) and communication (relationships and distribution). Both follow historical trends towards an increased use of hard information and less in-person interaction, which are accelerating rapidly. We evaluate more recent innovations, such as the combination of data abundance and artificial intelligence, and the rise of digital platforms. We argue that the rise of new communication channels can lead to the vertical and horizontal disintegration of the traditional bank business model. Specialized providers of financial services can chip away activities that do not rely on access to balance sheets, while platforms can interject themselves between banks and customers. We discuss limitations to these challenges to the traditional bank business model, and the resulting policy implications.
How Costly are Cultural Biases?
SSRN
We exploit a leading FinTech peer-to-peer lending platform paired with an automated robo-advising lending tool to test for and quantify the effects of cultural biases in large-stake risky choices for which the scope for statistical discrimination is minimal. Comparing the choices lenders make with those made by the robo-advising tool on their behalf, we find that both in-group vs. out-group discrimination and stereotypical discrimination are pervasive and economically sizable. Discrimination makes lenders worse off in terms of consumption utilityâ€"discriminating lenders face 32\% higher default rates and about 11% lower returns on the loans they issue to borrowers belonging to favored demographics, relative to borrowers in the discriminated groups. All the results are stronger for lenders who reside in regions where the salience of cultural biases is higher.
SSRN
We exploit a leading FinTech peer-to-peer lending platform paired with an automated robo-advising lending tool to test for and quantify the effects of cultural biases in large-stake risky choices for which the scope for statistical discrimination is minimal. Comparing the choices lenders make with those made by the robo-advising tool on their behalf, we find that both in-group vs. out-group discrimination and stereotypical discrimination are pervasive and economically sizable. Discrimination makes lenders worse off in terms of consumption utilityâ€"discriminating lenders face 32\% higher default rates and about 11% lower returns on the loans they issue to borrowers belonging to favored demographics, relative to borrowers in the discriminated groups. All the results are stronger for lenders who reside in regions where the salience of cultural biases is higher.
Interpretable Machine Learning for Diversified Portfolio Construction
SSRN
In this paper, the authors construct a pipeline to benchmark Hierarchical Risk Parity (HRP) relative to Equal Risk Contribution (ERC) as examples of diversification strategies allocating to liquid multi-asset futures markets with dynamic leverage ("volatility target"). The authors use interpretable machine learning concepts ("explainable AI") to compare the robustness of the strategies and to back out implicit rules for decision making. The empirical dataset consists of 17 equity index, government bond and commodity futures markets across 20 years. The two strategies are backtested for the empirical dataset and for about 100’000 bootstrapped datasets. XGBoost is used to regress the Calmar ratio spread between the two strategies against features of the bootstrapped datasets. Compared to ERC, HRP shows higher Calmar ratios and better matches the volatility target. Using Shapley values, the Calmar ratio spread can be attributed especially to univariate drawdown measures of the asset classes.
SSRN
In this paper, the authors construct a pipeline to benchmark Hierarchical Risk Parity (HRP) relative to Equal Risk Contribution (ERC) as examples of diversification strategies allocating to liquid multi-asset futures markets with dynamic leverage ("volatility target"). The authors use interpretable machine learning concepts ("explainable AI") to compare the robustness of the strategies and to back out implicit rules for decision making. The empirical dataset consists of 17 equity index, government bond and commodity futures markets across 20 years. The two strategies are backtested for the empirical dataset and for about 100’000 bootstrapped datasets. XGBoost is used to regress the Calmar ratio spread between the two strategies against features of the bootstrapped datasets. Compared to ERC, HRP shows higher Calmar ratios and better matches the volatility target. Using Shapley values, the Calmar ratio spread can be attributed especially to univariate drawdown measures of the asset classes.
Market Liquidity and Price Disparity: Evidence from Chinese Cross-listed Firms
SSRN
In this study, we investigate the association between stock liquidity and the H-share discount using a sample of Chinese cross-listed stocks in A- and H-shares markets. We examine the liquidity hypothesis by utilizing depth and trading activity and our results suggest that stocks with higher level in depth (active trading) of A-shares relative to H-shares are associated with less (more) H-share discount. Such effect is more pronounced in stocks that are non-state-owned and with low product market competition and ownership concentration. Moreover, we provide supportive evidence that the Stock Connect Program introduced in 2015 significantly dampens the association between stock liquidity and H-share discount. Overall, our findings highlight that the liquidity differences of cross-listed stocks explain large proportion of variation in price disparity.
SSRN
In this study, we investigate the association between stock liquidity and the H-share discount using a sample of Chinese cross-listed stocks in A- and H-shares markets. We examine the liquidity hypothesis by utilizing depth and trading activity and our results suggest that stocks with higher level in depth (active trading) of A-shares relative to H-shares are associated with less (more) H-share discount. Such effect is more pronounced in stocks that are non-state-owned and with low product market competition and ownership concentration. Moreover, we provide supportive evidence that the Stock Connect Program introduced in 2015 significantly dampens the association between stock liquidity and H-share discount. Overall, our findings highlight that the liquidity differences of cross-listed stocks explain large proportion of variation in price disparity.
Meet Markets: Investor Meetings and Expected Returns
SSRN
We show meetings of investors and firms convey information about expected returns. Investors frequently travel to meet in-person with firms before investing, and we show firms with abnormally frequent meetings predictably outperform firms with abnormally infrequent meetings by roughly 70-to-100 basis points per month. Abnormally frequent meetings also predict improvements in firms’ fundamental performance, suggesting our results stem from investors allocating time and attention to meetings with management from underpriced firms. Together, our findings highlight the usefulness of investors’ resource allocation decisions in expected return estimations, and provide insight into the multi-stage process investors undertake when forming portfolios.
SSRN
We show meetings of investors and firms convey information about expected returns. Investors frequently travel to meet in-person with firms before investing, and we show firms with abnormally frequent meetings predictably outperform firms with abnormally infrequent meetings by roughly 70-to-100 basis points per month. Abnormally frequent meetings also predict improvements in firms’ fundamental performance, suggesting our results stem from investors allocating time and attention to meetings with management from underpriced firms. Together, our findings highlight the usefulness of investors’ resource allocation decisions in expected return estimations, and provide insight into the multi-stage process investors undertake when forming portfolios.
Mobilising institutional investor capital for climate-aligned development
RePEC
Financing from institutional investors will be critical to achieving the sustainable development goals and curbing climate change. However, these large investors have been largely absent from multilateral initiatives to mobilise private capital. Partly as a result, such initiatives have been unable to reach the scale required for development finance to go "from billions to trillions". Successful mobilisation of private capital – including from institutional investors – has instead frequently taken place at the local level, by strategic investment funds and some green banks. At the same time, some institutional investors have been changing their modus operandi, from an intermediary to a collaborative model, and are re-localising their operations. The elimination of financial intermediaries with a short-term focus removes a bottleneck between two categories of long-term investors – institutional investors and multilateral finance institutions. That opens new opportunities for collaboration, as discussed in this paper.
RePEC
Financing from institutional investors will be critical to achieving the sustainable development goals and curbing climate change. However, these large investors have been largely absent from multilateral initiatives to mobilise private capital. Partly as a result, such initiatives have been unable to reach the scale required for development finance to go "from billions to trillions". Successful mobilisation of private capital – including from institutional investors – has instead frequently taken place at the local level, by strategic investment funds and some green banks. At the same time, some institutional investors have been changing their modus operandi, from an intermediary to a collaborative model, and are re-localising their operations. The elimination of financial intermediaries with a short-term focus removes a bottleneck between two categories of long-term investors – institutional investors and multilateral finance institutions. That opens new opportunities for collaboration, as discussed in this paper.
Monitoring Spillovers Between Competing Passive and Active Asset Managers
SSRN
The tremendous increase in ownership of US corporations by passive funds raises important issues for the corporate governance of firms. Passive funds primarily compete on both price and performance with other investment options â€" including active funds, direct investment, and risk-free holdings. There is an ongoing debate on whether this competition provides sufficient incentives to passive funds to invest in corporate governance. In this paper, we shed light on this debate by formally analyzing the monitoring incentives of passive and active funds when they compete with each other. We show that even passive funds find monitoring portfolio firms optimal in equilibrium. We further provide conditions for when passive and active fund monitoring are strategic complements, leading to monitoring of the same firms, or strategic substitutes, leading to monitoring of different firms. Overall, our results provide a more nuanced view of the role that passive funds play in firms' corporate governance.
SSRN
The tremendous increase in ownership of US corporations by passive funds raises important issues for the corporate governance of firms. Passive funds primarily compete on both price and performance with other investment options â€" including active funds, direct investment, and risk-free holdings. There is an ongoing debate on whether this competition provides sufficient incentives to passive funds to invest in corporate governance. In this paper, we shed light on this debate by formally analyzing the monitoring incentives of passive and active funds when they compete with each other. We show that even passive funds find monitoring portfolio firms optimal in equilibrium. We further provide conditions for when passive and active fund monitoring are strategic complements, leading to monitoring of the same firms, or strategic substitutes, leading to monitoring of different firms. Overall, our results provide a more nuanced view of the role that passive funds play in firms' corporate governance.
Not Just One Bet: Portfolio Managers' Cross Fund Risk-Taking
SSRN
This paper documents that mutual fund managers who experience distress in one fund tend to subsequently take on more risk in other funds they manage. Specifically, portfolio managers decrease the cash holdings and increase the systematic risk component in their linked funds. This increased risk-taking appears to be primarily motivated by managers' compensation contracts and is value-destroying for fund investors. The response to the distress shock is smaller for portfolio managers with long-term incentive compensation contracts, longer-tenured managers, and teams with female managers. These results highlight fund spillover effects through common portfolio managers and agency conflicts in the mutual fund industry.
SSRN
This paper documents that mutual fund managers who experience distress in one fund tend to subsequently take on more risk in other funds they manage. Specifically, portfolio managers decrease the cash holdings and increase the systematic risk component in their linked funds. This increased risk-taking appears to be primarily motivated by managers' compensation contracts and is value-destroying for fund investors. The response to the distress shock is smaller for portfolio managers with long-term incentive compensation contracts, longer-tenured managers, and teams with female managers. These results highlight fund spillover effects through common portfolio managers and agency conflicts in the mutual fund industry.
On Taking a Skewed Risk More Than Once
SSRN
This paper collects results on the repeated risk-taking of skewed risks. An extensive body of theoretical and experimental literature has shown that, in one-time decision situations, humans are skewness-seeking and dislike risks that feature unlikely but large losses (i.e., negatively skewed risks). We show that, contrary to intuition, the often-observed phenomenon of penny-pickingâ€"repeatedly taking negatively skewed risksâ€"is not at odds with skewness-seeking, but, to the contrary, may even be caused by it. The skewness of the distribution that results from repeatedly taking a skewed risk depends in non-trivial ways on the risk-taking strategy and may even differ in sign from that of the individual risk. With sufficient time available, every riskâ€"no matter how negatively skewedâ€"can be gambled in such a way that, in total, skewness is positive. Because recent work has shown that skewness is decisive whether risk is taken, this result may be important for economics and finance on a fundamental level.
SSRN
This paper collects results on the repeated risk-taking of skewed risks. An extensive body of theoretical and experimental literature has shown that, in one-time decision situations, humans are skewness-seeking and dislike risks that feature unlikely but large losses (i.e., negatively skewed risks). We show that, contrary to intuition, the often-observed phenomenon of penny-pickingâ€"repeatedly taking negatively skewed risksâ€"is not at odds with skewness-seeking, but, to the contrary, may even be caused by it. The skewness of the distribution that results from repeatedly taking a skewed risk depends in non-trivial ways on the risk-taking strategy and may even differ in sign from that of the individual risk. With sufficient time available, every riskâ€"no matter how negatively skewedâ€"can be gambled in such a way that, in total, skewness is positive. Because recent work has shown that skewness is decisive whether risk is taken, this result may be important for economics and finance on a fundamental level.
Optimal Asset Allocation Subject to Liquidity and Withdrawal Risks
SSRN
This study investigates the optimal asset allocation of a financial institution subject to liquidity risks and whose customers are free to withdraw their capital-guaranteed financial contracts at any time. Accounting for constraints on the solvency of the institution, we present a general optimization problem and provide a dynamic programming principle for the optimal dynamic investment strategies. Furthermore, we consider an explicit context, including the interest rate and credit intensity fluctuations, and show, by numerical results, that the optimal strategy improves the solvency and the asset returns of the institution compared to the baseline asset allocation.
SSRN
This study investigates the optimal asset allocation of a financial institution subject to liquidity risks and whose customers are free to withdraw their capital-guaranteed financial contracts at any time. Accounting for constraints on the solvency of the institution, we present a general optimization problem and provide a dynamic programming principle for the optimal dynamic investment strategies. Furthermore, we consider an explicit context, including the interest rate and credit intensity fluctuations, and show, by numerical results, that the optimal strategy improves the solvency and the asset returns of the institution compared to the baseline asset allocation.
Patent Trolls and Capital Structure Decisions in High-Tech Firms
SSRN
Under the growing threat of patent trolls, high-tech firms face substantial legal fees, increased cash flow volatility, and greater expected costs of distress. I show that the exposure to patent litigation leads to overly conservative capital structures in high-tech firms. My identification exploits a 2017 U.S. Supreme Court decision limiting the ability of patent trolls to seek favorable venue outside the defendant’s incorporating state. Following the decision, firms incorporated in states with strong anti-patent troll laws increased leverage. Treatment effects are stronger for high-tech firms, the premier targets of patent trolls. Decreased cash flow volatility, especially in treated firms closer to financial distress, provides a key channel for my results.
SSRN
Under the growing threat of patent trolls, high-tech firms face substantial legal fees, increased cash flow volatility, and greater expected costs of distress. I show that the exposure to patent litigation leads to overly conservative capital structures in high-tech firms. My identification exploits a 2017 U.S. Supreme Court decision limiting the ability of patent trolls to seek favorable venue outside the defendant’s incorporating state. Following the decision, firms incorporated in states with strong anti-patent troll laws increased leverage. Treatment effects are stronger for high-tech firms, the premier targets of patent trolls. Decreased cash flow volatility, especially in treated firms closer to financial distress, provides a key channel for my results.
Tax Expense Surprise and Emerging Market Equity Returns
SSRN
This paper investigates the relation between tax expense surprise and expected equity returns in emerging markets. Utilizing a broad sample of equities from 27 emerging countries, I document a strong and positive link between tax expense surprise and the cross-section of expected equity returns in these markets. Univariate portfolio analyses in the overall sample show that equities in the highest tax expense surprise quintile earn at least 8.64% higher annual return than stocks in the lowest tax expense surprise quintile. This relation remains robust even after controlling for other anomalies related to financial and tax variables in a regression framework. Thus, I conclude that tax expense surprise is a strong predictor of equity returns in emerging markets.
SSRN
This paper investigates the relation between tax expense surprise and expected equity returns in emerging markets. Utilizing a broad sample of equities from 27 emerging countries, I document a strong and positive link between tax expense surprise and the cross-section of expected equity returns in these markets. Univariate portfolio analyses in the overall sample show that equities in the highest tax expense surprise quintile earn at least 8.64% higher annual return than stocks in the lowest tax expense surprise quintile. This relation remains robust even after controlling for other anomalies related to financial and tax variables in a regression framework. Thus, I conclude that tax expense surprise is a strong predictor of equity returns in emerging markets.
The Benefits of Transaction-Level Data: The Case of Nielsen Scanner Data
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This study uses Nielsen scanner data to illustrate the benefits of transaction-level data. Specifically, we explore whether granular consumer purchases contain incremental value-relevant information about the corresponding manufacturers. Using weekly consumer purchases data generated by point-of-sale systems from 2006 to 2018, which capture around $2 trillion of U.S. retail sales, we construct a measure of aggregated consumer purchases at the firm-quarter level, and find that it strongly predicts manufacturer revenues. In addition, analyst forecasts of revenues have predictable errors and revisions, which implies that analysts do not fully incorporate the information in consumer purchases in a timely manner. Exploring investment implications, we find that hedge portfolios that buy (sell) stocks of firms with high (low) abnormal consumer purchases generate annualized returns on the magnitude of 14% to 19% depending on specification. This return predictability holds after controlling for risk factors and firm characteristics, and is robust across time. Finally, about 39% of the quarterly hedge returns are concentrated over the three-day window around earnings announcements, which suggests that the returns result from the correction of biased expectations rather than risk. These findings suggest that consumer purchases convey useful insights into firm fundamentals, and investors are slow to grasp this information, shedding light on the benefits of using transactional data for market participants.
SSRN
This study uses Nielsen scanner data to illustrate the benefits of transaction-level data. Specifically, we explore whether granular consumer purchases contain incremental value-relevant information about the corresponding manufacturers. Using weekly consumer purchases data generated by point-of-sale systems from 2006 to 2018, which capture around $2 trillion of U.S. retail sales, we construct a measure of aggregated consumer purchases at the firm-quarter level, and find that it strongly predicts manufacturer revenues. In addition, analyst forecasts of revenues have predictable errors and revisions, which implies that analysts do not fully incorporate the information in consumer purchases in a timely manner. Exploring investment implications, we find that hedge portfolios that buy (sell) stocks of firms with high (low) abnormal consumer purchases generate annualized returns on the magnitude of 14% to 19% depending on specification. This return predictability holds after controlling for risk factors and firm characteristics, and is robust across time. Finally, about 39% of the quarterly hedge returns are concentrated over the three-day window around earnings announcements, which suggests that the returns result from the correction of biased expectations rather than risk. These findings suggest that consumer purchases convey useful insights into firm fundamentals, and investors are slow to grasp this information, shedding light on the benefits of using transactional data for market participants.
The Bright Side of Financial Fragility
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We highlight an important but overlooked characteristic of financial fragility: “fragile†stocks are more liquid because they are sensitive to non-fundamental liquidity shocks. This makes them less sensitive to corporate actions with price impact and therefore affects firms’ incentives to engage in those actions. We show that fragile firms have lower share repurchases but invest more, the effects stronger for financially constrained firms. We establish causality by relying on exogenous changes in fragility induced by mergers of asset managers with portfolio overlap in the stocks. Our results suggest that financial fragility has direct but unexpected real implications for corporate actions.
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We highlight an important but overlooked characteristic of financial fragility: “fragile†stocks are more liquid because they are sensitive to non-fundamental liquidity shocks. This makes them less sensitive to corporate actions with price impact and therefore affects firms’ incentives to engage in those actions. We show that fragile firms have lower share repurchases but invest more, the effects stronger for financially constrained firms. We establish causality by relying on exogenous changes in fragility induced by mergers of asset managers with portfolio overlap in the stocks. Our results suggest that financial fragility has direct but unexpected real implications for corporate actions.
The Evolution of Monetary Rules with Financial Stability Considerations
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In this study, we estimate and investigate the evolution of monetary rules for China and the United States in the 21st century, with the aim to examine whether financial stability has been taken into consideration in practical monetary policy decision making. By applying and adapting the methodology of Frankel & Xie (2010), we could estimate structural breaks and split the whole time period into multiple monetary regimes based on the macroeconomic data of China from 2006 to 2019 and the U.S. from 2000 to 2019 respectively. Specifically, an extended Taylor rule with financial stability considerations as well as its evolution over time is estimated. It is found that China's monetary policy has put significant weight on the financial stress of the U.S. in the early years, before and during the global financial crisis. However, the coefficient on the U.S. financial stress has decreased since then, indicating less consideration for the U.S. financial stability and more emphasis on the domestic financial market by Chinese policymakers.
SSRN
In this study, we estimate and investigate the evolution of monetary rules for China and the United States in the 21st century, with the aim to examine whether financial stability has been taken into consideration in practical monetary policy decision making. By applying and adapting the methodology of Frankel & Xie (2010), we could estimate structural breaks and split the whole time period into multiple monetary regimes based on the macroeconomic data of China from 2006 to 2019 and the U.S. from 2000 to 2019 respectively. Specifically, an extended Taylor rule with financial stability considerations as well as its evolution over time is estimated. It is found that China's monetary policy has put significant weight on the financial stress of the U.S. in the early years, before and during the global financial crisis. However, the coefficient on the U.S. financial stress has decreased since then, indicating less consideration for the U.S. financial stability and more emphasis on the domestic financial market by Chinese policymakers.
The Fallacy of Maximizing Risk-Adjusted Returns
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Routinely, investors in managed funds or those they employ to assess managed funds, do so with certain parameters. Some of these parameters are not performance related (e.g., age of the fund, assets under management, type of fund) whereas there are almost always performance-related measures. Most of these performance measures pertain to perceptions of risk, often in the way of measures of variance with respect to return, worst-case performance depth as well as duration, consistency of returns, etc., collectively often falling under the moniker of “risk-adjusted returns.†Herein we demonstrate that, in the limit of continuing time, to maximize risk-adjusted returns is the same exercise as maximizing absolute returns, and hence, an investor in a program that doesn’t seek to maximize absolute returns is not getting what he should be getting in terms of the investment fees he pays.
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Routinely, investors in managed funds or those they employ to assess managed funds, do so with certain parameters. Some of these parameters are not performance related (e.g., age of the fund, assets under management, type of fund) whereas there are almost always performance-related measures. Most of these performance measures pertain to perceptions of risk, often in the way of measures of variance with respect to return, worst-case performance depth as well as duration, consistency of returns, etc., collectively often falling under the moniker of “risk-adjusted returns.†Herein we demonstrate that, in the limit of continuing time, to maximize risk-adjusted returns is the same exercise as maximizing absolute returns, and hence, an investor in a program that doesn’t seek to maximize absolute returns is not getting what he should be getting in terms of the investment fees he pays.
The Impact of Corporate Social Responsibility on Firm Value - The Role of Shareholder Preferences
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This article shows that corporate social responsibility (CSR) is positively related to firm value, given firms have shareholders who reveal a corresponding preference for social or environmental performance, as proxied by their quantifiable investment habits. I suspect that this corresponds to an appreciation by socially responsible investors and is reflected in higher value for firms with a stronger CSR performance. In line with this conjecture, I find a premium of 4% in relation to the average firm value for higher environmental performance and 3.5% for higher social performance. The results are consistent with theoretical concepts arguing that CSR expenditures can be compatible with value maximization if it is a response to shareholder preferences.
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This article shows that corporate social responsibility (CSR) is positively related to firm value, given firms have shareholders who reveal a corresponding preference for social or environmental performance, as proxied by their quantifiable investment habits. I suspect that this corresponds to an appreciation by socially responsible investors and is reflected in higher value for firms with a stronger CSR performance. In line with this conjecture, I find a premium of 4% in relation to the average firm value for higher environmental performance and 3.5% for higher social performance. The results are consistent with theoretical concepts arguing that CSR expenditures can be compatible with value maximization if it is a response to shareholder preferences.
Theory of Leveraged Portfolio Selection Under Liquidity Risk
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We study the impact of liquidity in optimal portfolio choice under leveraging to improve risk-adjusted and absolute returns. We consider a quasi-elastic market with continuous trading where temporary liquidity costs are sufficiently large relative to permanent impact. We show analytically that the Sharpe-maximizing unlevered portfolio is no longer a tangency portfolio. As target mean increases, required portfolio-leverage increases at an increasing-rate, while Sharpe-Leverage frontiers are progressively-dominated. Moreover, security-market relationships are no-longer linear and the usual proportionate-leveraging is not an optimal strategy. We develop insights for choosing return targets for leverage-constrained investors, and provide computational analyses to highlight the analytical findings.
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We study the impact of liquidity in optimal portfolio choice under leveraging to improve risk-adjusted and absolute returns. We consider a quasi-elastic market with continuous trading where temporary liquidity costs are sufficiently large relative to permanent impact. We show analytically that the Sharpe-maximizing unlevered portfolio is no longer a tangency portfolio. As target mean increases, required portfolio-leverage increases at an increasing-rate, while Sharpe-Leverage frontiers are progressively-dominated. Moreover, security-market relationships are no-longer linear and the usual proportionate-leveraging is not an optimal strategy. We develop insights for choosing return targets for leverage-constrained investors, and provide computational analyses to highlight the analytical findings.
Trust As an Entry Barrier: Evidence from FinTech Adoption
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This paper studies the role of trust in incumbent lenders (banks) as an entry barrier to emerging FinTech lenders in the credit markets. The empirical setting exploits the outburst of the Wells Fargo scandal as a negative shock to the trust in banks. Using a difference-in-differences framework, I find that increased exposure to the Wells Fargo scandal leads to an increase in the probability of borrowers using FinTech as mortgage originators. Utilizing political affiliation to proxy for the magnitude of trust erosion in banks in a triple-differences specification, I find that, conditional on the same exposure to the scandal, a county experiencing more trust erosion has a larger increase in FinTech share relative to a county experiencing less trust erosion.
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This paper studies the role of trust in incumbent lenders (banks) as an entry barrier to emerging FinTech lenders in the credit markets. The empirical setting exploits the outburst of the Wells Fargo scandal as a negative shock to the trust in banks. Using a difference-in-differences framework, I find that increased exposure to the Wells Fargo scandal leads to an increase in the probability of borrowers using FinTech as mortgage originators. Utilizing political affiliation to proxy for the magnitude of trust erosion in banks in a triple-differences specification, I find that, conditional on the same exposure to the scandal, a county experiencing more trust erosion has a larger increase in FinTech share relative to a county experiencing less trust erosion.
What Can Analysts Learn from Artificial Intelligence about Fundamental Analysis?
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Taking the perspective of an equity investor seeking to maximize risk-adjusted returns through financial statement analysis, we apply a machine learning algorithm to estimate Nissim and Penman’s (2001) structural decomposition framework of profitability. Our approach explicitly takes account of the nonlinearities that precluded Nissim and Penman from estimating their framework. We first forecast profitability and then estimate intrinsic values using different subsets of Nissim and Penman’s framework and different fundamental analysis design choices; we find that trading on these estimates generates substantial risk-adjusted returns. Choices that improve performance include increasingly granular ratio disaggregation and long-horizon forecasts of operating performance. Perhaps surprisingly, we find only weak evidence of benefits from a fundamental analysis that incorporates historical financial statement information beyond the current-period information or focuses only on core items. While taking account of non-linearities improves model performance for all firms, the effect is strongest for small, loss-making, technology, and financially distressed firms.
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Taking the perspective of an equity investor seeking to maximize risk-adjusted returns through financial statement analysis, we apply a machine learning algorithm to estimate Nissim and Penman’s (2001) structural decomposition framework of profitability. Our approach explicitly takes account of the nonlinearities that precluded Nissim and Penman from estimating their framework. We first forecast profitability and then estimate intrinsic values using different subsets of Nissim and Penman’s framework and different fundamental analysis design choices; we find that trading on these estimates generates substantial risk-adjusted returns. Choices that improve performance include increasingly granular ratio disaggregation and long-horizon forecasts of operating performance. Perhaps surprisingly, we find only weak evidence of benefits from a fundamental analysis that incorporates historical financial statement information beyond the current-period information or focuses only on core items. While taking account of non-linearities improves model performance for all firms, the effect is strongest for small, loss-making, technology, and financially distressed firms.
What Do Questions Reveal? Analyst Topic-Specific Skill and Forecast Accuracy
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Existing studies identify analyst skills by ex post evaluation of their outputs (forecasts and recommendations), which are contaminated by luck and noise. Using the premises that questions reveal interest and that interest and practice lead to skill, we construct an ex ante analyst topic-specific skill measure based on the frequency of topic-specific questions that analysts raise during previous earnings conference calls. In a supply chain information setting, we show that analysts with supply-chain-specific skill experience a greater improvement in forecast accuracy relative to their peers when the firms they cover establish relationships with important supply chain partners. Analysts with supply-chain-specific skill improve information efficiency in capital markets, as shown by faster information diffusion speed along the supply chain and stronger market reactions to their recommendation updates.
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Existing studies identify analyst skills by ex post evaluation of their outputs (forecasts and recommendations), which are contaminated by luck and noise. Using the premises that questions reveal interest and that interest and practice lead to skill, we construct an ex ante analyst topic-specific skill measure based on the frequency of topic-specific questions that analysts raise during previous earnings conference calls. In a supply chain information setting, we show that analysts with supply-chain-specific skill experience a greater improvement in forecast accuracy relative to their peers when the firms they cover establish relationships with important supply chain partners. Analysts with supply-chain-specific skill improve information efficiency in capital markets, as shown by faster information diffusion speed along the supply chain and stronger market reactions to their recommendation updates.
