Research articles for the 2019-06-13
SSRN
This article investigates the association between the CSR and marginal credit costs of European companies. We provide instance for a negative association based on a variety of model specifications and fine-grained measures for CSR. These results can be explained in light of the increasing relevance of socially responsible investors for financing costs of companies. We further apply the risk management perspective on CSR to credit market and show that the insurance-like property of CSR is especially relevant for companies in relative financial distress as measured by its interest coverage ratio. This study also examines the association between CSR assurance and credit costs and provides evidence that creditors reward such nonfinancial insurance through reduced required rate of returns. Finally, we contribute to the corporate governance literature by modelling the association between different board characteristics and credit costs.
SSRN
We propose a new framework to value employee stock options (ESOs) that captures multiple exercises of different quantities over time. We also model the ESO holder's job termination risk and incorporate its impact on the payoffs of both vested and unvested ESOs. Numerical methods based on Fourier transform and finite differences are developed and implemented to solve the associated systems of PDEs. In addition, we introduce a new valuation method based on maturity randomization that yields analytic formulae for vested and unvested ESO costs. We examine the cost impact of job termination risk, exercise intensity, and various contractual features.
SSRN
Information dissemination and aggregation are key economic functions of ï¬nancial markets. How intelligent do traders have to be for the complex task of aggregating diverse information (i.e., approximate the predictions of the rational expectations equilibrium) in a competitive double auction market? An apparent ex-ante answer is: intelligent enough to perform the bootstrap operation necessary for the taskâ€"to somehow arrive at prices that are needed to generate those very prices. Constructing a path to such equilibrium through rational behavior has remained beyond what we know of human cognitive abilities. Yet, laboratory experiments report that proï¬t motivated human traders are able to aggregate information in some, but not all, market environments (Plott and Sunder 1988, Forsythe and Lundholm 1990). Algorithmic agents have the potential to yield insights into how simple individual behavior may perform this complex market function as an emergent phenomenon. We report on a computational experiment with markets populated by algorithmic traders who follow cognitively simple heuristics humans are known to use. These markets, too, converge to rational expectations equilibria in environments in which human markets converge, albeit slowly and noisily. The results suggest that high level of individual intelligence or rationality is not necessary for efficient outcomes to emerge at the market level; the structure of the market itself is a source of rationality observed in the outcomes.
SSRN
U.S. colleges and universities have for some time joined the privatization movement where university functions and assets are turned over to private contractors. Here we present a case of a 50-year comprehensive energy concession agreement by The Ohio State University that generated an up-front payment exceeding a billion dollars. The agreement, with many principles and design practices borrowed directly from economic regulation of public utilities and against the backdrop of the literature on privatization and contract theory, provides an insightful case that informs these literature, as well as future privatization efforts by universities and other large-scale public organizations.
SSRN
Exotic high tech metals such as rare earth oxides, titanium and nickel wire are increasingly important to semi-conductor, aerospace and high end defence technology R & D and production. As a result â€" while not market traded and therefore sporadic order dependent and highly volatile â€" prices of these commodities have been rising on average over the past decade. The metals trading subsidiary of an international group acquired over 6 M metres of nickel wire which at the current market price of about 300 EUR per metre is worth over 1.6 B EUR. The firm wished to raise from 300 M to 1B EUR in the capital markets in order to purchase a variety of these high tech metals to take advantage over the medium term (5 to 10 years) of generally rising prices. After a brief description of the current state of the exotic high tech metalss markets, this paper treats the technical pricing and default risk analysis of an example 350 M EUR 7 year amortized corporate bond issue backed by a nickel wire inventory and subsequent high tech metal trading as collateral. Topics covered include security modelling with high tech metal collateral, the design of 100% risk free securities with third party derivatives and security pricing and trading methodology. The complex stochastic analysis and Monte Carlo simulation analysis presented is based in part on specially developed modelling of the nickel wire catalogue price and third party price projections for rare earth oxides and titanium. The analysis is based on 10 year (2008â€"2017) daily market data and supports an optimistic view in that after accounting for all ongoing costs we find a zero default probability for the bond issue â€" a situation seldom seen to accompany its stipulated 12% internal rate of return.
SSRN
In this note, we present a simple numerical example to illustrate the case where the growth rate for the Free Cash Flow (FCF) gU is greater than but different from the growth rate for the Cash Flow to Debt (CFD) gD. Here we assume that the value of the appropriate discount rate for the tax shield KTS equals the cost of debt KD.
SSRN
Emerging markets have common weaknesses in their financial market development. Financial development is one institutional force that shapes financing and governance of firms in emerging markets. Debt and equity are alternative governance instruments. Trade credit is part of debt and therefore should be treated as such in corporate governance. We used a fixed effect regression of financial sector development and trade credit of firms listed on the Johannesburg Stock Exchange to ascertain the relationship of financial sector development and trade credit. We also analyzed the Socially Responsible Index (SRI) which measures corporate governance. We find that good corporate governance practices do not result in substituting of trade credit, despite its high implicit costs, with bank loans for working capital financing.
SSRN
This paper aims to analyze the derivatives disclosure in banks’ annual risk reports. In this paper, the author uses content analysis to examine the qualitative and quantitative profiles of the derivatives disclosure at a cross-country level, with particular reference to credit derivatives. The empirical research is conducted on a sample of large European banks. The paper also shows that there is room to improve various aspects of derivatives disclosure, and provides some useful insights for further research.
SSRN
We propose a novel probabilistic model to facilitate the learning of multivariate tail dependence of multiple financial assets. Our method allows one to construct from known random vectors, e.g., standard normal, sophisticated joint heavy-tailed random vectors featuring not only distinct marginal tail heaviness, but also flexible tail dependence structure. The novelty lies in that pairwise tail dependence between any two dimensions is modeled separately from their correlation, and can vary respectively according to its own parameter rather than the correlation parameter, which is an essential advantage over many commonly used methods such as multivariate $t$ or elliptical distribution. It is also intuitive to interpret, easy to track, and simple to sample comparing to the copula approach. We show its flexible tail dependence structure through simulation. Coupled with a GARCH model to eliminate serial dependence of each individual asset return series, we use this novel method to model and forecast multivariate conditional distribution of stock returns, and obtain notable performance improvements in multi-dimensional coverage tests. Besides, our empirical finding about the asymmetry of tails of the idiosyncratic component as well as the whole market is interesting and worth to be well studied in the future.
arXiv
We propose a novel approach to estimate asset pricing models for individual stock returns that takes advantage of the vast amount of conditioning information, while keeping a fully flexible form and accounting for time-variation. Our general non-linear asset pricing model is estimated with deep neural networks applied to all U.S. equity data combined with a substantial set of macroeconomic and firm-specific information. We estimate the stochastic discount factor that explains all asset returns from the conditional moment constraints implied by no-arbitrage. Our asset pricing model outperforms out-of-sample all other benchmark approaches in terms of Sharpe ratio, explained variation and pricing errors. We trace its superior performance to including the no-arbitrage constraint in the estimation and to accounting for macroeconomic conditions and non-linear interactions between firm-specific characteristics. Our generative adversarial network enforces no-arbitrage by identifying the portfolio strategies with the most pricing information. Our recurrent Long-Short-Term-Memory network finds a small set of hidden economic state processes. A feedforward network captures the non-linear effects of the conditioning variables. Our model allows us to identify the key factors that drive asset prices and generate profitable investment strategies.
arXiv
We present an advance bubble detection methodology based on the Log Periodic Power Law Singularity (LPPLS) confidence indicator for the early causal identification of positive and negative bubbles in the Chinese stock market using the daily data on the Shanghai Shenzhen CSI 300 stock market index from January 2002 through April 2018. We account for the damping condition of LPPLS model in the search space and implement the stricter filter conditions for the qualification of the valid LPPLS fits by taking account of the maximum relative error, performing the Lomb log-periodic test of the detrended residual, and unit-root tests of the logarithmic residual based on both the Phillips-Perron test and Dickey-Fuller test to improve the performance of LPPLS confidence indicator. Our analysis shows that the LPPLS detection strategy diagnoses the positive bubbles and negative bubbles corresponding to well-known historical events, implying the detection strategy based on the LPPLS confidence indicator has an outstanding performance to identify the bubbles in advance. We find that the probability density distribution of the estimated beginning time of bubbles appears to be skewed and the mass of the distribution is concentrated on the area where the price starts to have an obvious super-exponentially growth. This study is the first work in the literature that identifies the existence of bubbles in the Chinese stock market using the daily data of CSI 300 index with the advance bubble detection methodology of LPPLS confidence indicator. We have shown that it is possible to detect the potential positive and negative bubbles and crashes ahead of time, which in turn limits the bubble sizes and eventually minimizes the damages from the bubble crash.
arXiv
In this study, we perform a novel analysis of the 2015 financial bubble in the Chinese stock market by calibrating the Log Periodic Power Law Singularity (LPPLS) model to two important Chinese stock indices, SSEC and SZSC, from early 2014 to June 2015. The back tests of the 2015 Chinese stock market bubbles indicates that the LPPLS model can readily detect the bubble behavior of the faster-than-exponential increase corrected by the accelerating logarithm-periodic oscillations in the 2015 Chinese Stock market. The existence of log-periodicity is detected by applying the Lomb spectral analysis on the detrended residuals. The Ornstein-Uhlenbeck property and the stationarity of the LPPLS fitting residuals are confirmed by the two Unit-root tests (Philips-Perron test and Dickery-Fuller test). According to our analysis, the actual critical day t_c can be well predicted by the LPPLS model as far back as two months before the actual bubble crash. Compared to the traditional optimization method used in the LPPLS model, we find the covariance matrix adaptation evolution strategy (CMA-ES) to have a significantly lower computation cost, and thus recommend this as a better alternative algorithm for LPPLS model fit. Furthermore, in the LPPLS fitting with expanding windows, the gap (tc -t2) shows a significant decrease when the end day t2 approaches the actual bubble crash time. The change rate of the gap (tc-t2) may be used as an additional indicator besides the key indicator tc to improve the prediction of bubble burst.
SSRN
The authors employ content analysis to conduct an empirical study on a sample of large European banks. The authors propose a hybrid scoring model for the assessment of derivative disclosure in banking institutions. The methodology employed in this research is able to capture a considerable amount of information because it combines the characteristics of a quantitative and qualitative analysis. This article provides evidences that banks differ in their derivative reporting, although they are subject to similar regulatory requirements and accounting standards.
SSRN
We examine whether firms undertaking an initial public offering (IPO) exhibit less earnings management when individual investment bankers have prior experience in public accounting. Although auditors are primarily responsible for providing external monitoring of the financial reporting process, individual bankers also have strong incentives to improve accounting quality in firms going public. In exploiting unique disclosures of bankers’ identities and characteristics in China, our analysis indicates that individual bankers with early-career experience as auditors constrain issuer firms’ earnings management through both accruals and real activities. Consistent with expectations, this evidence is more pronounced if the audit firm that formerly employed the banker is larger; has expertise in the IPO firm’s industry; is permitted to audit listed companies; and was previously subject to a regulatory sanction. Collectively, our analysis implies that public accounting experience is valuable for financial intermediaries, particularly when this experience stems from working at a high-quality audit firm.
SSRN
Rather than focusing on issuer characteristics as done in prior studies, this paper presents a macro-level perspective on the role of societal trust in Sukuk issuance. Using a comprehensive sample of global Sukuk issuances spanning over the period of 2001 to 2018 and controlling for issuer characteristics and Sukuk features, we find that the level of societal trust in a country significantly and positively influences the amount of Sukuk issued. These results hold after controlling for time, industry and country-specific fixed effects and are robust under various settings. Moreover, we also unfold a moderating role of societal trust in (i) mitigating the investor risk perception associated with the Sukuk investments reflected by a decline in the yield to maturity and yield spread on Sukuk issuances and (ii) improving the financial performance of the issuing firms.
SSRN
The 2018 U.S.-China trade war has spurred wide-spread debates about the effects of Chinese imports on U.S. society. We examine the effect of United States’ conferral of Permanent Normal Trade Relations (PNTR) on China â€" a policy that eliminates the uncertainty of future tariff increases for Chinese goods â€" on U.S. firm innovation. We find a significant increase in the number of patents and patent citations for U.S. firms affected by PNTR relative to other firms. This result is stronger for firms located in the coastal area, and for firms in industries that experience a greater increase in Chinese goods following PNTR. Our evidence suggests that PNTR induces U.S. firms to innovate more.
SSRN
In this paper we consider the pricing of variable annuities (VAs) with guaranteed minimum withdrawal benefits. We consider two pricing approaches, the classical risk-neutral approach and the benchmark approach, and we examine the associated static and optimal behaviors of both the investor and insurer. The first model considered is the so-called minimal market model, where pricing is achieved using the benchmark approach. The benchmark approach was introduced by Platen in 2001 and has received wide acceptance in the finance community. Under this approach, valuing an asset involves determining the minimum-valued replicating portfolio, with reference to the growth optimal portfolio under the real-world probability measure, and it both subsumes classical risk-neutral pricing as a particular case and extends it to situations where risk-neutral pricing is impossible. The second model is the Black-Scholes model for the equity index, where the pricing of contracts is performed within the risk-neutral framework. Crucially, we demonstrate that when the insurer prices and reserves using the Black-Scholes model, while the insured employs a dynamic withdrawal strategy based on the minimal market model, the insurer may be underestimating the value and associated reserves of the contract.
SSRN
Does financial development affect economic growth through its impact on the accumulation of capital inputs or by boosting total factor productivity growth? We use a new data set on output, physical, and human capital inputs for the U.S. states to study this question. Unlike previous cross-country research that tries to disentangle the channels through which financial development impacts growth, we use a plausibly exogenous measure of financial development: the timing of banking deregulation across states during the period 1970-2000. At the same time, our new data set allows us to go beyond what was previously done in the state banking deregulation literature and identify whether finance impacts states' input accumulation or TFP growth. We find, in line with existing cross-country studies, that deregulation boosts growth by accelerating both TFP growth and the accumulation of physical capital without having any impact on human capital. In contrast to the cross-country studies, we also find that the effects of deregulation are largely independent of states' initial level of development; both rich and poor states grow faster after deregulation. Additionally, since our data set breaks down aggregate output into three sectors: agriculture, manufacturing, and the remaining industries, we are able to show that deregulation accelerates the growth of productivity in manufacturing. This last finding answers an important critique of the banking deregulation studies which asserts that observed growth effects may be coming from the growth of the financial industry itself and not from the beneficial effect of finance on other industries, such as manufacturing.
SSRN
The first section (from accounting to corporate finance) answers the following questions: What is the net income? Is it what the shareholders “earnâ€, what the company “earnsâ€, what someone “earnsâ€? What does shareholders´ equity mean? Is it money? We show that net income is an arbitrary number which depends on several decisions on the accounting of expenses and revenues. We use three different definitions of cash flow: equity cash flow (ECF), free cash flow (FCF) and capital cash flow (CCF) and answer to the question: When is net income equal to the equity cash flow?The second section (shareholder value creation and shareholders return) defines, analyzes and calculates shareholder value creation. It also differentiates the expected return from the required return. The all-shareholder return is the return that all the shareholders of a company had in a period. It is equal to the hypothetical return of a unique shareholder of the company. It is also the return of a shareholder that always had a constant proportion of the shares. The all-period shareholder return is the return that a shareholder that maintained the shares for the whole period had. There are many all-period shareholder returns, depending on the actions of the shareholder during the period: fraction of dividends reinvested, fraction of shares sold when the company repurchased them, number of shares subscribed when the company increased capital… Most databases provide a specific all-period shareholder return valid for a shareholder that reinvested 100% of the dividends, did not sell any share in repurchases and did not subscribe any new share when the company increased capital. In many situations, there are substantial differences among these returns.The third section (topics and real cases on valuation) shows that It is a big mistake to use betas calculated from historical data to compute the required return to equity for seven reasons. It also shows two real valuation cases of companies.The fourth section (other finance and investing topics) shows that the Market Portfolio is NOT efficient and that it has been very easy to beat the S&P500 in 2000-2018. It also shows confusions, errors and inconsistencies of several utilities regulators when calculating WACC using CAPM.
arXiv
We propose a general non-linear order book model that is built from the individual behaviours of the agents. Our framework encompasses Markovian and Hawkes based models. Under mild assumptions, we prove original results on the ergodicity and diffusivity of such system. Then we provide closed form formulas for various quantities of interest: stationary distribution of the best bid and ask quantities, spread, liquidity fluctuations and price volatility. These formulas are expressed in terms of individual order flows of market participants. Our approach enables us to establish a ranking methodology for the market makers with respect to the quality of their trading.
SSRN
The ageing of the Swiss population poses a challenge for the financing of the Swiss Old Age Insurance (AHV). It has therefore been suggested that the Swiss National Bank (SNB) transfer part of its assets to a new AHV demography fund, the income of which could be used to finance the AHV. In this paper it is argued that the SNB could contribute CHF 100 to 150 billion worth of assets to the AHV without restricting the scope for monetary policy.
SSRN
A new method for identifying bull and bear financial market regimes is proposed, related to a classic algorithm for picking turning points in the business cycle. Our approach uses only a single discrete parameter, adjusted to the periodicity of the data, which largely removes subjectivity from the regime identification process and limits data snooping. Applying it to the Dow Jones Industrial Average index data, we show its capability of obtaining a classification similar to competing multi-parameter methods, without imposing any conditions on regime duration or amplitude. Our algorithm can be easily applied across different asset classes, where its direct competitors may fail, as we show in an out-of-sample identification example for oil price series and an exchange rate.
SSRN
This paper examines how a tournament among CEOs to progress within the CEO labor market changes their tendency toward corporate hedging policies. We exploit the textual analysis of 10-Ks to generate corporate hedging proxies. We find that the likelihood and intensity to hedge increase as the CEO labor market tournament prize augments. We also find a positive relation between industry tournament incentives (ITI) and foreign exchange (FX) hedging and interest rate hedging, but cannot detect a significant link between ITI and commodity hedging. We discover the mitigating impact of the corporate hedging on the amplifier effect of ITI on the cost of debt and stock price crash risk could be the possible reasons for the positive relation between ITI and corporate hedging. Lastly, findings show that the association between ITI and corporate hedging is more pronounced for the firms having financial constraint and operating in industries having higher CEO mobility, and when CEO is not a founder or not of the retirement age.
SSRN
We study an information-sale problem where a monopolist proxy advisor sells voting recommendations to a firm's shareholders for corporate voting. We make a distinction between a recommendation that is unbiased and one that is desired by the shareholders. Under some ideal conditions, the proxy advisor provides both unbiased and desirable voting advice. However, when these conditions are not satisfied (as they typically will not be), the proxy advisor sends biased voting advice, which may or may not be desired by the shareholders. Overall, our paper points out the inherent limitations of the proxy advisor as information intermediaries in corporate voting.
arXiv
Information transfer between time series is calculated by using the asymmetric information-theoretic measure known as transfer entropy. Geweke's autoregressive formulation of Granger causality is used to find linear transfer entropy, and Schreiber's general, non-parametric, information-theoretic formulation is used to detect non-linear transfer entropy.
We first validate these measures against synthetic data. Then we apply these measures to detect causality between social sentiment and cryptocurrency prices. We perform significance tests by comparing the information transfer against a null hypothesis, determined via shuffled time series, and calculate the Z-score. We also investigate different approaches for partitioning in nonparametric density estimation which can improve the significance of results.
Using these techniques on sentiment and price data over a 48-month period to August 2018, for four major cryptocurrencies, namely bitcoin (BTC), ripple (XRP), litecoin (LTC) and ethereum (ETH), we detect significant information transfer, on hourly timescales, in directions of both sentiment to price and of price to sentiment. We report the scale of non-linear causality to be an order of magnitude greater than linear causality.
SSRN
Exploiting the staggered enactment of countryâ€level mergers and acquisitions (M&A) law as an exogenous increase in corporate takeover threat, this paper examines how a disciplinary market for corporate control affects accounting conservatism. Following M&A law adoption, we find increased accounting conservatism, with more pronounced effects in countries with weak shareholder protection and in those experiencing larger growth in takeover activity. Further analysis reveals that elevated takeover threats increase conservatism through changes in capital structure and investment decisions as well as improvements in board monitoring. Our findings highlight the importance of the market for corporate control in shaping financialâ€reporting outcome.
SSRN
The aim of this chapter is to investigate market risk disclosure in banking. The author employs content analysis to conduct an empirical study on a sample of the ten largest Italian banks. The study provides evidence that banks differ in their market risk reporting, even though they are subject to similar regulatory requirements and accounting standards. It also shows that there is room to improve various aspects of risk disclosure, and provides some useful insights for further research.
arXiv
Application of neural network architectures for financial prediction has been actively studied in recent years. This paper presents a comparative study that investigates and compares feed-forward neural network (FNN) and adaptive neural fuzzy inference system (ANFIS) on stock prediction using fundamental financial ratios. The study is designed to evaluate the performance of each architecture based on the relative return of the selected portfolios with respect to the benchmark stock index. The results show that both architectures possess the ability to separate winners and losers from a sample universe of stocks, and the selected portfolios outperform the benchmark. Our study argues that FNN shows superior performance over ANFIS.
SSRN
We examine how parameter learning amplifies the impact of macroeconomic shocks on equity prices and quantities in a standard production economy where a representative agent has Epstein-Zin preferences. An investor observes technology shocks that follow a regime-switching process, but does not know the underlying model parameters governing the short-term and long-run perspectives of economic growth. We show that rational parameter learning endogenously generates long-run productivity and consumption risks that help explain a wide array of dynamic pricing phenomena. The asset pricing implications of subjective long-run risks crucially depend on the introduction of a procyclical dividend process consistent with the data.
arXiv
In this paper I re-examine the COMPAS recidivism score and criminal history data collected by ProPublica in 2016, which has fueled intense debate and research in the nascent field of `algorithmic fairness' or `fair machine learning' over the past three years. ProPublica's COMPAS data is used in an ever-increasing number of studies to test various definitions and methodologies of algorithmic fairness. This paper takes a closer look at the actual datasets put together by ProPublica. In particular, I examine the distribution of defendants across COMPAS screening dates and find that ProPublica made an important data processing mistake when it created some of the key datasets most often used by other researchers. Specifically, the datasets built to study the likelihood of recidivism within two years of the original COMPAS screening date. As I show in this paper, ProPublica made a mistake implementing the two-year sample cutoff rule for recidivists in such datasets (whereas it implemented an appropriate two-year sample cutoff rule for non-recidivists). As a result, ProPublica incorrectly kept a disproportionate share of recidivists. This data processing mistake leads to biased two-year recidivism datasets, with artificially high recidivism rates. This also affects the positive and negative predictive values. On the other hand, this data processing mistake does not impact some of the key statistical measures highlighted by ProPublica and other researchers, such as the false positive and false negative rates, nor the overall accuracy.
SSRN
In this paper, we present how ideas from artificial intelligence field can be utilized in proxy modeling problem that is faced by actuarial departments of life insurance companies. The current approaches are reviewed, exposing their incapability to fully mimic the complexity and non-linearity of cash-flow projection models. In order to increase the quality of proxy models, we propose to apply selected machine learning algorithms as well as provide an overview of the theory behind them and present the numerical results with a comparison of model errors for different estimators. The study is performed on real data generated by a large reinsurance company. The text can serve as a guideline for companies willing to introduce machine learning algorithms in their proxy modeling processes.
SSRN
We propose a conditional quantile model that can learn long term and short term memories of sequential data. It builds on sequential neural networks and yet outputs interpretable dynamics. We apply the model to asset return time series across eleven asset classes using historical data from the 1960s to 2018 and reveals that it extracts not only the serial dependence structure in conditional volatility but also the memories buried deep in the tails of historical prices. We further evaluate its Value-at-Risk forecasts against a wide range of prevailing models. Our model outperforms the GARCH family as well as models using filtered historical simulation, conditional extreme value theory, and dynamic quantile regression. These studies indicate that conditional quantiles of asset return have persistent sources of risk that are not coming from those responsible for volatility clustering. These findings could have important implications for risk management in general and tail risk forecast in particular.
SSRN
This paper proposes three new measures of real estate illiquidity and provides an in-depth examination of their importance for returns. Using UK data from 1997Q1-2017Q3, we show that real estate illiquidity is priced, and negatively related to returns. Using regional models of real estate markets, we show that illiquidity shocks both significantly depress returns and are economically meaningful with these shocks explaining up to 25% of regional return variation. Allowing for inter-sectoral spillovers, we document substantial differences in the influence of regional illiquidity throughout time. The implication is that real estate investors can diversify away, or hedge against, systemic and inter-sectoral exposure.
arXiv
In the past decade, Bitcoin as an emerging asset class has gained widespread public attention because of their extraordinary returns in phases of extreme price growth and their unpredictable massive crashes. We apply the log-periodic power law singularity (LPPLS) confidence indicator as a diagnostic tool for identifying bubbles using the daily data on Bitcoin price in the past two years. We find that the LPPLS confidence indicator based on the daily Bitcoin price data fails to provide effective warnings for detecting the bubbles when the Bitcoin price suffers from a large fluctuation in a short time, especially for positive bubbles. In order to diagnose the existence of bubbles and accurately predict the bubble crashes in the cryptocurrency market, this study proposes an adaptive multilevel time series detection methodology based on the LPPLS model and finer (than daily) timescale for the Bitcoin price data. We adopt two levels of time series, 1 hour and 30 minutes, to demonstrate the adaptive multilevel time series detection methodology. The results show that the LPPLS confidence indicator based on this new method is an outstanding instrument to effectively detect the bubbles and accurately forecast the bubble crashes, even if a bubble exists in a short time. In addition, we discover that the short-term LPPLS confidence indicator highly sensitive to the extreme fluctuations of Bitcoin price can provide some useful insights into the bubble status on a shorter time scale - on a day to week scale, and the long-term LPPLS confidence indicator has a stable performance in terms of effectively monitoring the bubble status on a longer time scale - on a week to month scale. The adaptive multilevel time series detection methodology can provide real-time detection of bubbles and advanced forecast of crashes to warn of the imminent risk.
SSRN
We propose how to quantify high-frequency market sentiment using high-frequency news from NASDAQ news platform and support vector machine classifiers. News arrive at markets randomly and the resulting news sentiment behaves like a stochastic process. To characterize the joint evolution of sentiment, price, and volatility, we introduce a unified continuous-time sentiment-driven stochastic volatility model. We provide closed-form formulas for moments of the volatility and news sentiment processes and study the news impact. Further, we implement a simulation-based method to calibrate the parameters. Empirically, we document that news sentiment raises the threshold of volatility reversion, sustaining high market volatility.
arXiv
We propose a novel estimation approach for the covariance matrix based on the $l_1$-regularized approximate factor model. Our sparse approximate factor (SAF) covariance estimator allows for the existence of weak factors and hence relaxes the pervasiveness assumption generally adopted for the standard approximate factor model. We prove consistency of the covariance matrix estimator under the Frobenius norm as well as the consistency of the factor loadings and the factors.
Our Monte Carlo simulations reveal that the SAF covariance estimator has superior properties in finite samples for low and high dimensions and different designs of the covariance matrix. Moreover, in an out-of-sample portfolio forecasting application the estimator uniformly outperforms alternative portfolio strategies based on alternative covariance estimation approaches and modeling strategies including the $1/N$-strategy.
arXiv
We investigate the performance of dynamic portfolios constructed using more than 21,000 technical trading rules on 12 categorical and country-specific markets over the 2004-2015 study period, on rolling forward structures of different lengths. We also introduce a discrete false discovery rate (DFRD+/-) method for controlling data snooping bias. Compared to the existing methods, DFRD+/- is adaptive and more powerful, and accommodates for discrete p-values. The profitability, persistence and robustness of the technical rules are examined. Technical analysis still has short-term value in advanced, emerging and frontier markets. Financial stress, the economic environment and market development seem to affect the performance of trading rules. A cross-validation exercise highlights the importance of frequent rebalancing and the variability of profitability in trading with technical analysis.
SSRN
Since VIX options started trading in 2006, many researchers have tried to build a model that jointly and exactly calibrates to the prices of S&P 500 (SPX) options, VIX futures and VIX options. So far the best attempts, which used parametric continuous-time jump-diffusion models on the SPX, only produced an approximate fit. In this article we solve this longstanding puzzle using a nonparametric discrete-time model. Given a VIX future maturity T1, we build a joint probability measure on the SPX at T1, the VIX at T1, and the SPX at T2 = T1 + 30 days which is perfectly calibrated to the SPX smiles at T1 and T2, and the VIX future and VIX smile at T1. Our model satisfies the martingality constraint on the SPX as well as the requirement that the VIX at T1 is the implied volatility of the 30-day log-contract on the SPX. We prove by duality that the existence of such a model means that the SPX and VIX markets are jointly arbitrage-free.The joint calibration puzzle is cast as a dispersion-constrained martingale transport problem which is solved using (an extension of) the Sinkhorn algorithm, in the spirit of De March and Henry-Labordere (2019). The algorithm identifies joint SPX/VIX arbitrages should they arise. Our numerical experiments show that the algorithm performs very well in both low and high volatility regimes. Finally we explain how to handle the fact that the VIX future and SPX option monthly maturities do not perfectly coincide, and how to extend the two-maturity model to include all available monthly maturities.
SSRN
A growing literature in economics explores the relationship between personal experiences with the business cycle and belief/preference formation. There exists substantial evidence using national variation in business cycles that personal experiences hold substantial weight in decision-making. However, the use of national aggregates limits researchers to the use of variation in decisions across birth-cohorts. Using state-level personal income for the majority of the 20th century, I investigate whether individual investment decisions are altered by sub-national economic fluctuations. Along with providing evidence that preferences/beliefs about investment begin to form in late childhood, my results suggest that children who grew up in states with lower average personal income invest less in risky assets throughout their lives, invest more in property, and are less likely to be self employed.
SSRN
This paper examines all of the banks that submitted sufficient Call Report data to the FDIC for the quarters ended September 30, 2015 through December 31, 2018. We compared the current December 31, 2018, loan loss reserve percentage to calculated actual write-off experience, as described below, over the 42-month period ending on December 31, 2018 (the Recovery Period) and the 42-month period ending on December 31, 2011 (the Great Recession Period). The 42-month period was selected based on an average maturity calculation from amounts reported on the December 31, 2018, Call Report.For each selected bank, we totaled the bad debt write-offs (net of recoveries) from the call reports submitted to the FDIC for the quarters ended September 30, 2015 through December 31, 2018 and divided the result by the average gross loans and leases balance over that same period. We performed the same procedures for the quarters ended September 30, 2008 through December 31, 2011 (Post Recession).For each period, we then broke the data down by size decile based on December 31, 2018 total asset balances. We found that, for the Recovery period, at all size deciles, the median current bad debt reserve balance exceeds the calculated 2015 to 2018 net write-off bad debt experience. However, compared to the Post Recession period (from September 30, 2008 to December 31, 2011) the banking system appears under-reserved by from 50% (for the smallest banks) to 500% (for the largest banks). Portfolio duration will impact the calculation of expected reserves. However, given the shorter portfolio duration in banking relative to a full economic cycle, the expected reserve may be insufficient if the full economic cycle is not considered.
SSRN
We examine professional connections among executives and analysts formed through overlapping historical employment. Analysts with professional connections to coverage firms have more accurate earnings forecasts, and issue more informative buy and sell recommendations. These analysts are more likely to participate, be chosen first, and ask more questions during earnings conference calls and analyst/investor days. Homophily based on gender, age and ethnicity is orthogonal to professional connections. Brokers attract greater trade commissions on stocks covered by connected analysts. Firms benefit through securing research coverage and invitations to broker-hosted investor conferences emulating from these connections.
arXiv
Different investment strategies are adopted in short-term and long-term depending on the time scales, even though time scales are adhoc in nature. Empirical mode decomposition based Hurst exponent analysis and variance technique have been applied to identify the time scales for short-term and long-term investment from the decomposed intrinsic mode functions(IMF). Hurst exponent ($H$) is around 0.5 for the IMFs with time scales from few days to 3 months, and $H\geq0.75$ for the IMFs with the time scales $\geq5$ months. Short term time series [$X_{ST}(t)$] with time scales from few days to 3 months and $H~0.5$ and long term time series [$X_{LT}(t)$] with time scales $\geq5$ and $H\geq0.75$, which represent the dynamics of the market, are constructed from the IMFs. The $X_{ST}(t)$ and $X_{LT}(t)$ show that the market is random in short-term and correlated in long term. The study also show that the $X_{LT}(t)$ is correlated with fundamentals of the company. The analysis will be useful for investors to design the investment and trading strategy.
SSRN
Following the 2004 introduction of VIX futures, they have become an increasingly important hedging instrument and aid for portfolio diversification. We examine changes in the futures basis which, owing to their unique characteristics, can be interpreted as changes in expectations of future VIX (“fearâ€) levels. We find that higher levels of VIX are associated with a narrowing of the futures basis, suggesting that investors view “fear†as transitory, and a flatter forward curve. We propose news sentiment as one plausible explanation for changes in the basis. A wider (narrower) basis accompanies the more positive (negative) news associated with a falling (rising) VIX index.
SSRN
We propose a 4-factor model by adding an additional trend factor to Liu, Stambaugh and Yuan’s (2018; LSY-3) 3-factor model: market, size and value. Since individual investors contribute about 80% of the trading volume in China, the trend factor captures well the resulting important price and volume trends, and has a monthly Sharpe ratio of 0.48, much greater than those of the market (0.11), size (0.19) and value (0.28). The proposed 4-factor model explains all reported Chinese anomalies, including turnover and reversal unexplained previously by LSY-3. Moreover, the model explains well mutual fund returns, working as an analogue of Carhart 4-factor model in China.
SSRN
Recent advances in machine learning are finding commercial applications across many industries, not least the finance industry. This paper focuses on applications in one of the core functions of finance, the investment process. This includes return forecasting, risk modelling and portfolio construction. The study evaluates the current state of the art through an extensive review of recent literature. Themes and technologies are identified and classified, and the key use cases highlighted. Quantitative investing, traditionally a leading field in adopting new techniques is found to be the most common source of use cases in the emerging literature.
SSRN
We find that investors tend to hold the same securities as their parents. Instrumental variables that exploit social networks and a natural experiment based on mergers allow us to attribute the security-choice correlation to social influence within families. This influence runs not only from parents to children, but also in the opposite direction. Security holdings correlate more when family members are more likely to communicate and when they are more susceptible to social influence. The identical security holdings that social influence generates largely explain why risk-return profiles of household portfolios correlate across generations.
