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Trending academic research:
See also: Most popular SSRN papers
InfoTrad: An Extensive R Package for Estimating the Probability of Informed Trading abstract The purpose of this paper is to introduce the R package InfoTrad for the estimating probability of informed trading (PIN) initially proposed by Easley et al. (1996). PIN is a popular information asymmetry measure that proxies the proportion of informed traders in the market. This study provides a short survey on alternative estimation techniques for PIN. In the literature, there are two main problems documented for estimating PIN. The sequential trading structure proposed by Easley et al. (1996) and later extended by Easley et al. (2002) is prone to sample selection bias for large cap stocks due to floating point exception. This problem is solved by the different factorizations provided by Easley et al. (2010) (EHO factorization) and Lin and Ke (2011) (LK factorization). In addition, the estimates are prone to bias due to boundary solutions. A grid-search algorithm (YZ algorithm) is proposed by Yan and Zhang (2012) to overcome the bias introduced due to boundary estimates. In recent years, clustering algorithms have become popular due to their flexibility in quickly handling large data sets. Gan et al. (2015) propose an algorithm (GAN algorithm) to estimate PIN using hierarchical agglomerative clustering. The package InfoTrad implements parameter estimations for calculating PIN with two general likelihood factorizations EHO and LK. In addition, those factorizations can be used to estimate PIN through YZ algorithm and GAN algorithm.
Asset Allocation Strategies, the 1/N Rule, and Data Snooping abstract Using a series of tests based on White's (2000) "Reality Check," we assess the out-of-sample performance of a large class of portfolio strategies relative to the naive 1/N rule and control for data-snooping bias. We examine if 23 basic strategies and more than 5,000 extended strategies outperform the 1/N rule in terms of the Sharpe ratio and the certainty-equivalent return (CEQ). Our empirical results based on various stock portfolios suggest that only a few strategies out-perform the 1/N rule after we control for data-snooping bias and transaction costs. Thus, this paper has implications for market efficiency and asset allocation.
Screening and Adverse Selection in Frictional Markets abstract We incorporate a search-theoretic model of imperfect competition into an otherwise standard model of asymmetric information with unrestricted contracts. We develop a methodology that allows for a sharp analytical characterization of the unique equilibrium, and then use this characterization to explore the interaction between adverse selection, screening, and imperfect competition. On the positive side, we show how the structure of equilibrium contracts — and hence the relationship between an agent’s type, the quantity he trades, and the corresponding price — are jointly determined by the severity of adverse selection and the concentration of market power. This suggests that quantifying the effects of adverse selection requires controlling for the market structure. On the normative side, we show that increasing competition and reducing informational asymmetries can be detrimental to welfare. This suggests that recent attempts to increase competition and reduce opacity in markets that suffer from adverse selection could potentially have negative, unforeseen consequences.
How fast does the clock of Finance run? - A time-definition enforcing scale invariance abstract A symmetry-guided time redefinition may enhance and simplify analyses of historical series displaying recurrent patterns. Enforcing a simple-scaling symmetry with Hurst exponent 1/2 and the requirement of increments' stationarity, we identify a time-definition protocol in the financial case. The novel time scale, constructed through a systematic application of the Kolmogorov-Smirnov criterion to extensive data of the S&P500 index, lays a bridge between the regime of minutes and that of several days in physical time. It allows us to quantify the duration of periods in which the market is inactive, like amid nights, and to optimally exploit the statistical information contained in the series. The overall strategy leads to a significant reduction of multiscaling features, once the moments of the return probability density function are analyzed versus the novel time.
Generating High Investment Performance with State Space Model abstract This paper proposes a unified approach to creating high performance portfolios with various desirable properties for investors.
Particularly, we provide a new interpretation and the resulting formulations for state space models to attain our investment objectives, which are possibly specified as achieving target mean-variance portfolios or Sharpe ratios, and generating alphas (additional returns) over benchmark indexes.
More concretely, in state space modeling to financial time-series data, we can apply the system model to representing portfolio weight processes with various constraints, as well as the standard underlying state variables such as volatility processes.
Moreover, we may formulate the observation model to stand for target value processes with non-linear functions of observed and latent variables.
Numerical experiments demonstrate the effectiveness of our methodology through alpha-creation against S&P 500 futures, and substantial improvement of the performance on mean-variance portfolios.
Long and Short Memory in Economics: Fractional-Order Difference and Differentiation abstract Long and short memory in economic processes is usually described by the so-called discrete fractional differencing and fractional integration. We prove that the discrete fractional differencing and integration are the Grunwald-Letnikov fractional differences of non-integer order d. Equations of ARIMA(p,d,q) and ARFIMA(p,d,q) models are the fractional-order difference equations with the Grunwald-Letnikov differences of order d. We prove that the long and short memory with power law should be described by the exact fractional-order differences, for which the Fourier transform demonstrates the power law exactly. The fractional differencing and the Grunwald-Letnikov fractional differences cannot give exact results for the long and short memory with power law, since the Fourier transform of these discrete operators satisfy the power law in the neighborhood of zero only. We prove that the economic processes with the continuous time long and short memory, which is characterized by the power law, should be described by the fractional differential equations.
Economic Accelerator with Memory: Discrete Time Approach abstract Accelerators with power-law memory are proposed in the framework of the discrete time approach. To describe discrete accelerators we use the capital stock adjustment principle, which has been suggested by Matthews.The suggested discrete accelerators with memory describe the economic processes with the power-law memory and the periodic sharp splashes (kicks). In continuous time approach the memory is described by fractional-order differential equations. In discrete time approach the accelerators with memory are described by discrete maps with memory, which are derived from the fractional-order differential equation without approximations. In order to derive these maps we use the equivalence of fractional-order differential equations and the Volterra integral equations.
Systematic Tail Risk abstract We propose new systematic tail risk measures constructed using two different approaches. The first extends the canonical downside beta and co-moment measures, while the second is based on the sensitivity of stock returns to innovations in market crash risk. Both tail risk measures are associated with a significantly positive risk premium after controlling for other measures of downside risk, including downside beta, co-skewness and co-kurtosis. Using these measures, we examine the relevance of the tail risk premium for investors with different investment horizons.
Information Aggregation and Data Snooping abstract This paper studies the interaction between information aggregation and data snooping in the context of predicting stock returns. Using simulations, we demonstrate that the aggregation of predictors by standard techniques amplifies the distortions of test sizes produced by data snooping. The proposed alternative aggregation technique, which is a modification of 3PRF/PLS, penalizes likely spurious predictors and thereby mitigates data snooping concerns. We illustrate our approach by applying various aggregation methods to three sets of return predictors suggested in the literature. We find that the forecasting ability of combined predictors in two cases cannot be fully explained by data snooping.
Number of Pages in PDF File: 43
Keywords: predictability of returns, data snooping, forecast combination, PLS, 3PRF
JEL Classification: G17, C58
Causality Networks of Financial Assets abstract Through financial network analysis we ascertain the existence of important causal behavior among certain financial assets, as inferred by eight different causality methods. Our results contradict the Efficient Market Hypothesis and opens new horizons for further investigation and possible arbitrage opportunities. Moreover, we find some evidence that two of the causality methods used, at least to some extent, could warn us about the financial crisis of 2007-2009. Furthermore, we test the similarity percentage of the eight causality methods and we find that the most similar pair of causality-induced networks is on average less than 50\% similar throughout the time period examined, rendering thus the comparability and substitutability among those causality methods rather dubious. We also rank both the causal relationships and the assets in terms of overall causality exertion and we find that there is an underlying bonds regime almost monopolising in some cases the realm of causality. Finally, we observe a recurring pattern of Oil's rising role as the financial network faces the Chinese stock market crash.
Salience Theory and Stock Prices: Empirical Evidence abstract We present empirical evidence on the asset pricing implications of salience theory. In our model, investors overweight salient past returns when forming expectations about future returns. Consequently, investors are attracted to stocks with salient upsides, which are overvalued and earn low subsequent returns. Conversely, stocks with salient downsides are undervalued and yield high future returns. We find strong empirical support for these predictions in the cross-section of U.S. stocks. The salience effect is stronger among stocks with greater limits to arbitrage and during high-sentiment periods and not explained by common risk factors and proxies for lottery demand and investor attention.
A Practical Approach to Measuring Market Impact in Investment Management abstract Market-impact costs are a widely discussed issue in investment management, but are rarely quantified in a way that is useful for investors in making manager-selection decisions. In this article, I adapt academic research to create a simple formula for measuring market-impact effects from the outside in, and discuss the implications of these costs on capacity, pricing, and other issues. In particular, there is a contradiction between the size at which funds close and their fee levels, suggesting that asset managers either do not understand market impact well, or alternatively that they believe they cannot generate alpha in excess of their fees. The paper also outlines directions for future research.
Geographies of High Frequency Trading - Algorithmic Capitalism and Its Contradictory Elements abstract This paper investigates the geographies of high frequency trading. Today shares shift hands within microseconds, giving rise to a form of financial geographies termed algorithmic capitalism. This notion refers to the different spatio-temporalities produced by high frequency trading, under the valuation of time. As high frequency trading accelerates financial markets, the paper examines the spatiotemporalities of automated trading by the ways in which the speed of knowledge exploitation in financial markets is not only of interest, but also the expansion between different temporalities. The paper demonstrates how the intensification of time – space compression produces radical new dynamics in the financial market and develops information rent in HFT as convertible to a time rent and a spatio-temporal rent. The final section discusses whether high frequency trading only responds to crises in microseconds or constitutes them. It argues that automated trading will not only contribute to accelerate crises, but also deepen them by the ways in which it differentiates the dynamics between financial, fixed and productive capital.
Pointwise Arbitrage Pricing Theory in Discrete Time abstract We develop a robust framework for pricing and hedging of derivative securities in discrete-time financial markets. We consider markets with both dynamically and statically traded assets and make minimal measurability assumptions. We obtain an abstract (pointwise) Fundamental Theorem of Asset Pricing and Pricing--Hedging Duality. Our results are general and in particular include so-called model independent results of Acciao et al. (2016), Burzoni et al. (2016) as well as seminal results of Dalang et al. (1990) in a classical probabilistic approach. Our analysis is scenario--based: a model specification is equivalent to a choice of scenarios to be considered. The choice can vary between all scenarios and the set of scenarios charged by a given probability measure. In this way, our framework interpolates between a model with universally acceptable broad assumptions and a model based on a specific probabilistic view of future asset dynamics.
Emerging Derivatives Markets? abstract Only 10% of global derivatives turnover is in contracts denominated in the currency of an emerging market economy (EME), much lower than the share of these economies in global GDP or world trade. Derivatives in EME currencies also tend to be less complex and more likely to be traded outside the home economy than those in advanced economy currencies. Differences persist even if we control for key drivers of derivatives turnover such as the size of the bond market, the openness of the capital account, the amount of foreign trade and the size of external liabilities. Instead, the small size of EME derivatives markets appears to reflect differences in per capita income. Large external asset holdings by residents of a country go hand in hand with lower turnover, perhaps because they are used as a hedge against country risk.
Exploring Commodity Trading Activity: An Integrated Analysis of Swaps and Futures abstract This paper presents an analysis of new, regulatory data on commodity swaps, focused on West Texas Intermediate (WTI) crude oil. We find that commercial end-users have a much larger footprint in the WTI swaps space than financial end-users do. Commercials have a much larger exposure in swaps than in futures and are net short in both markets. Financial end-users are smaller in swaps than in futures and are net long in both markets. Swap Dealers perform a substantial amount of intermediation among WTI longs, WTI shorts, and index investors; consequently, net dealer exposure to hedge in futures markets is far less than the gross swap exposure.
A Study on Role of NSE in Capital Market abstract Stock Exchange is a hub of primary and secondary market playing a crucial role in the economy. Stock exchange provides a place to the buyers and sellers of the shares and securities. For this purpose National stock exchange was established by the leading institutions in mid 1990s with the main aim to provide a modern and fully automated screen based trading system with national reach. National Stock Exchange has set up facility that serves as a model for securities industry in terms of system and procedures. Presently, the Capital Market segment of National Stock Exchange provides an efficient and transparent platform for trading of equity, preference shares, debentures, exchange traded funds as well as retail government securities. Many researches had taken place with respect to individual market prices, market efficiency, market index, S&P CNX Nifty etc; plantar of studies in context to National Stock Exchange has been conducted. But the Role of NSE in Capital Market is a study which focuses on the importance of establishment of National Stock Exchange.
Outcomes of Investing in OTC Stocks abstract This paper analyzes three aspects of over-the-counter (OTC) stocks: (1) the recent trends in the OTC stock market structure and size; (2) the documented properties of OTC stocks; and (3) the differences in returns based on investor and stock characteristics. Approximately 10,000 OTC stocks were quoted at the end of 2013 through 2015, generating a total trading volume of over $200 billion per year. Dollar volume has grown substantially since 2012 and is now concentrated in the segment of the OTC market with no requirements of registration or reporting to the U.S. Securities and Exchange Commission (SEC). A synthesis of recent academic literature reveals troubling properties of OTC stocks. Academic studies find that OTC stocks tend to be highly illiquid; are frequent targets of alleged market manipulation; generate negative and volatile investment returns on average; and rarely grow into a large company or transition to listing on a stock exchange. Moreover, these properties tend to worsen when the OTC company has fewer disclosure-related eligibility requirements. I examine the relationship between OTC investor demographics and investment outcomes using a proprietary database of transaction-level OTC data with confidential investor information. Analysis of 1.8 million trades by over 200,000 individual investors confirms that the typical OTC investment return is severely negative. Investor outcomes worsen for OTC stocks that experience a promotional campaign or have weaker disclosure-related eligibility requirements. Demographic analysis reveals that older, retired, low-income, and less educated investors experience significantly poorer outcomes in OTC stock markets. Given that retail investors are the predominant owners of OTC stocks, and the documented trend towards less transparent OTC companies, the results of this study have important implications for investor protection.
Higher Moment Risk Premiums for the Crude Oil Market: A Downside and Upside Conditional Decomposition abstract Relying on options written on the USO, an exchange traded fund tracking the daily price changes of the WTI light sweet crude oil, we extract variance and skew risk premiums in a model-free way. We further decompose these risk premiums into downside and upside conditional components and show that they are time varying; that they can be partially explained by USO excess returns and, more importantly, these decomposed risk premiums enable a much better prediction of USO excess returns than the standard, or undecomposed, variance and skew risk premiums.
Slipping through the Cracks: Detecting Manipulation in Regional Commodity Markets abstract Between 2010 and 2014, the regional price of aluminum in the United States (Midwest premium) increased threefold. We argue that the Midwest premium was likely manipulated during this period through the exercise of market power in the aluminum storage market. We first use a difference-in-differences model to show that there was a statistically significant increase of $0.07 per pound in the regional price of aluminum relative to the regional price of a production complement, copper. We then use several instrumental variables to show that this increase was driven by a single financial company’s accumulation of an unprecedented level of aluminum inventories in Detroit. Since this scheme targeted the regional price of aluminum, regulators who monitored only spot and futures prices would not have noticed anything peculiar. We therefore present an algorithm for real-time detection of similar manipulation schemes in regional commodity markets. The algorithm confirms the existence of a structural break in the U.S. aluminum market in late 2011. Using the algorithm, regulators could have detected the scheme as early as December 2012, more than six months before it was publicized by an article in The New York Times. We also apply the algorithm to another suspected case of regional price manipulation in the European aluminum market and find a similar break in 2011, suggesting the scheme may have been implemented beyond the United States.
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