We look at the effect of the tick size changes on the TOPIX 100 index names made by the Tokyo Stock Exchange on Jan-14-2014 and Jul-22-2104. The intended consequence of the change is price improvement and shorter time to execution. We look at security level metrics that include the spread, trading volume, number of trades and the size of trades to establish whether this goal is accomplished. An unintended effect might be the reduction in execution sizes, which would then mean that institutions with large orders would have greater difficulty in sourcing liquidity. We look at a sample of real orders to see if the execution costs have gone up across the orders since the implementation of this change.

We study the mechanisms that affect how securities are traded on an exchange, before delving into the specifics of the TSE tick size events. Some of the topics we explore are: The Venue Menu and How to Increase Revenue; To Automate or Not to Automate; Microstructure under the Microscope; The Price of Connections to High (and Faraway) Places; Speed Thrills but Kills; Pick a Size for the Perfect Tick; TSE Tick Size Experiments, Then and Now; Sergey Bubka and the Regulators; Bird`s Eye View; Deep Dive; Possibilities for a Deeper Dive; Does Tick Size Matter? Tick Size Does Matter!

We develop extensions to auction theory results that are useful in real life scenarios.

1. Since valuations are generally positive we first develop approximations using the log-normal distribution. This would be useful for many finance related auction settings since asset prices are usually non-negative.

2. We formulate a positive symmetric discrete distribution, which is likely to be followed by the total number of auction participants, and incorporate this into auction theory results.

3. We develop extensions when the valuations of the bidders are interdependent and incorporate all the results developed into a final combined realistic setting.

4. Our methods can be a practical tool for bidders and auction sellers to maximize their profits. The models developed here could be potentially useful for inventory estimation and for wholesale procurement of financial instruments and also non-financial commodities.

All the propositions are new results and they refer to existing results which are stated as Lemmas.

The Receiver Operating Characteristic (ROC) curve is a representation of the statistical information discovered in binary classification problems and is a key concept in machine learning and data science. This paper studies the statistical properties of ROC curves and its implication on model selection. We analyze the implications of different models of incentive heterogeneity and information asymmetry on the relation between human decisions and the ROC curves. Our theoretical discussion is illustrated in the context of a large data set of pregnancy outcomes and doctor diagnosis from the Pre-Pregnancy Checkups of reproductive age couples in Henan Province provided by the Chinese Ministry of Health.

In this feasibility study, the impact of academic research from social sciences and humanities on technological innovation is explored through a study of citations patterns of journal articles in patents. Specifically we focus on citations of journals from the field of environmental economics in patents included in an American patent database (USPTO). Three decades of patents have led to a small set of journal articles (85) that are being cited from the field of environmental economics. While this route of measuring how academic research is validated through its role in stimulating technological progress may be rather limited (based on this first exploration), it may still point to a valuable and interesting topic for further research.

The central problems of Development Economics are the explanation of the gross disparities in the global distribution, $\cal{D}$, of economic performance, $\cal{E}$, and the persistence, $\cal{P}$, of said distribution. Douglass North argued, epigrammatically, that institutions, $\cal{I}$, are the rules of the game, meaning that $\cal{I}$ determines or at least constrains $\cal{E}$. This promised to explain $\cal{D}$. 65,000 citations later, the central problems remain unsolved. North's institutions are informal, slowly changing cultural norms as well as roads, guilds, and formal legislation that may change overnight. This definition, mixing the static and the dynamic, is unsuited for use in a necessarily time dependent theory of developing economies. We offer here a suitably precise definition of $\cal{I}$, a dynamical theory of economic development, a new measure of the economy, an explanation of $\cal{P}$, a bivariate model that explains half of $\cal{D}$, and a critical reconsideration of North's epigram.

Inspired by Gambetta's theory on the origins of the mafia in Sicily, we report a geo-concentrating phenomenon of scams in China, and propose a novel economic explanation. Our analysis has some policy implications.

We show that the lack of arbitrage in a model with both fixed and proportional transaction costs is equivalent to the existence of a family of absolutely continuous single-step probability measures, together with an adapted process with values between the bid-ask spreads that satisfies the martingale property with respect to each of the measures. This extends Harrison and Pliska's classical Fundamental Theorem of Asset Pricing to the case of combined fixed and proportional transaction costs.

We present a deep long short-term memory (LSTM)-based neural network for predicting asset prices, together with a successful trading strategy for generating profits based on the model's predictions. Our work is motivated by the fact that the effectiveness of any prediction model is inherently coupled to the trading strategy it is used with, and vise versa. This highlights the difficulty in developing models and strategies which are jointly optimal, but also points to avenues of investigation which are broader than prevailing approaches. Our LSTM model is structurally simple and generates predictions based on price observations over a modest number of past trading days. The model's architecture is tuned to promote profitability, as opposed to accuracy, under a strategy that does not trade simply based on whether the price is predicted to rise or fall, but rather takes advantage of the distribution of predicted returns, and the fact that a prediction's position within that distribution carries useful information about the expected profitability of a trade. The proposed model and trading strategy were tested on the S&P 500, Dow Jones Industrial Average (DJIA), NASDAQ and Russel 2000 stock indices, and achieved cumulative returns of 340%, 185%, 371% and 360%, respectively, over 2010-2018, far outperforming the benchmark buy-and-hold strategy as well as other recent efforts.

We utilize a fundamentally different model of trading costs to look at the effect of the opening of the Hong Kong Shanghai Connect that links the stock exchanges in the two cities, arguably the biggest event in international business and finance since Christopher Columbus set sail for India. We design a novel methodology that compensates for the lack of data on trading costs in China. We estimate trading costs across similar positions on the dual listed set of securities in Hong Kong and China, hoping to provide useful pieces of information to help scale 'The Great Wall of Chinese Securities Trading Costs'. We then compare actual and estimated trading costs on a sample of real orders across the Hong Kong securities in the dual listed pair to establish the accuracy of our measurements.

The primary question we seek to address is 'Which market would be better to trade to gain exposure to the same (or similar) set of securities or sectors?' We find that trading costs on Shanghai, which might have been lower than Hong Kong, might have become higher leading up to the Connect. What remains to be seen is whether this increase in trading costs is a temporary equilibrium due to the frenzy to gain exposure to Chinese securities or whether this phenomenon will persist once the two markets start becoming more and more tightly coupled.

It would be interesting to see if this pioneering policy will lead to securities exchanges across the globe linking up one another, creating a trade anything, anywhere and anytime marketplace. Looking beyond mere trading costs, such studies can be used to gather some evidence on what effect the mode of governance and other aspects of life in one country have on another country, once they start joining up their financial markets.

Mutual information minimum spanning trees are used to explore nonlinear dependencies on Brazilian equity network in the periods from June/01/2015 to January/26/2016, in which Brazil was under the government of President Dilma Rousseff, and from January/27/2016 to September/08/2016 which includes the government transition from President Dilma Rousseff to President Michel Temer. Minimum spanning trees from mutual information and linear correlation between stocks returns were obtained and compared. Mutual information minimum spanning trees present higher degree of robustness and evidence of power law tail in the weighted degree distribution, indicating more risk in terms of volatility transmission than it is expected by the analysis based on linear correlation. In particular, a remarkable increase of stock returns nonlinear dependencies indicates that the period including the government transition is more risky in terms of volatility transmission network structure. Also, we found evidence of network structure and stock performance relationship. Besides, those results emphasize the usefulness of mutual information network analysis for identification of Financial Markets features due to nonlinear dependencies.

Online reviews are feedback voluntarily posted by consumers about their consumption experiences. This feedback indicates customer attitudes such as affection, awareness and faith towards a brand or a firm and demonstrates inherent connections with a company's future sales, cash flow and stock pricing. However, the predicting power of online reviews for long-term returns on stocks, especially at the individual level, has received little research attention, making a comprehensive exploration necessary to resolve existing debates. In this paper, which is based exclusively on online reviews, a methodology framework for predicting long-term returns of individual stocks with competent performance is established. Specifically, 6,246 features of 13 categories inferred from more than 18 million product reviews are selected to build the prediction models. With the best classifier selected from cross-validation tests, a satisfactory increase in accuracy, 13.94%, was achieved compared to the cutting-edge solution with 10 technical indicators being features, representing an 18.28% improvement relative to the random value. The robustness of our model is further evaluated and testified in realistic scenarios. It is thus confirmed for the first time that long-term returns of individual stocks can be predicted by online reviews. This study provides new opportunities for investors with respect to long-term investments in individual stocks.

Bitcoin is a digital financial asset that is devoid of a central authority. This makes it distinct from traditional financial assets in a number of ways. For instance, the total number of tokens is limited and it has not explicit use value. Nonetheless, little is know whether it obeys the same stylized facts found in traditional financial assets. Here we test bitcoin for a set of these stylized facts and conclude that it behaves statistically as most of other assets. For instance, it exhibits aggregational Gaussianity and fluctuation scaling. Moreover, we show by an analogy with natural occurring quakes that bitcoin obeys both the Omori and Gutenberg-Richter laws. Finally, we show that the global persistence, originally defined for spin systems, presents a power law behavior with exponent similar to that found in stock markets.

The equity risk premium puzzle is that the return on equities has far exceeded the average return on short-term risk-free debt and cannot be explained by conventional representative-agent consumption based equilibrium models. We review a few attempts done over the years to explain this anomaly: 1. Inclusion of highly unlikely events with low probability (Ugly state along with Good and Bad), or market crashes / Black Swans. 2. Slow moving habit, or time-varying subsistence level, added to the basic power utility function. 3. A separation of the inter-temporal elasticity of substitution and risk aversion, combined with long run risks which captures time varying economic uncertainty. We explore whether a fusion of the above approaches supplemented with better methods to handle the below reservations would provide a more realistic and yet tractable framework to tackle the various conundrums in the social sciences: 1. Unlimited ability of individuals to invest as compared to their ability to consume. 2. Lack of an objective measuring stick of value 3. Unintended consequences due to the dynamic nature of social systems 4. Relaxation of the transversality condition to avoid the formation of asset price bubbles 5. How durable is durable? Accounting for durable goods since nothing lasts forever The world we live in produces fascinating phenomenon despite (or perhaps, due to) being a hotchpotch of varying doses of the above elements. The rationale for a unified theory is that beauty can emerge from chaos since the best test for a stew is its taste. Many long standing puzzles seem to have been resolved using different techniques. The various explanations need to stand the test of time before acceptance; but then unexpected outcomes set in and new puzzles emerge. As real analysis and limits tell us: We are getting Closer and Closer; Yet it seems we are still Far Far Away...

This article analyzes the role of Finnish regulation in achieving the broadband penetration goals defined by the National Regulatory Authority. It is well known that in the absence of regulatory mitigation the population density has a positive effect on broadband diffusion. Hence, we measure the effect of the population density on the determinants of broadband diffusion throughout the postal codes of Finland via Geographically Weighted Regression. We suggest that the main determinants of broadband diffusion and the population density follow a spatial pattern that is either concentric with a weak/medium/strong strength or non-concentric convex/concave. Based on 10 patterns, we argue that the Finnish spectrum policy encouraged Mobile Network Operators to satisfy ambitious Universal Service Obligations without the need for a Universal Service Fund. Spectrum auctions facilitated infrastructure-based competition via equitable spectrum allocation and coverage obligation delivery via low-fee licenses. However, state subsidies for fiber deployment did not attract investment from nationwide operators due to mobile preference. These subsidies encouraged demand-driven investment, leading to the emergence of fiber consumer cooperatives. To explain this emergence, we show that when population density decreases, the level of mobile service quality decreases and community commitment increases. Hence, we recommend regulators implementing market-driven strategies for 5G to stimulate local investment. For example, by allocating the 3.5 GHz and higher bands partly through local light licensing.