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Using a large proprietary database of intraday high‐frequency trading, we investigate the trading strategies of institutional investors in dealing with the negative environmental event disclosure of listed companies and their impact on markets, aiming to reveal the mechanism of the lack of "green efficiency" in China's capital market from the perspective of institutional investors. The results show that institutional investors react to negative environmental events prior to the announcements, indicating premature information leakage in the market; in addition, their trading behaviors mitigate the immediate effect of negative environmental event announcements on stock price. After the event is disclosed, institutional investors engage in short‐term selling and long‐term buy and hold. This trading strategy undermines the irrational selling of individual investors in the event of disclosure, short‐term decline in stock price, and long‐term reversal of market overreaction. In a China context, institutional investors generally take environmental information into consideration. However, they fail to recognize the long‐term value effect of negative environmental events and instead cater to trading strategies towards market volatility. [ABSTRACT FROM AUTHOR]

Deep learning is an effective approach to solving image recognition problems. People draw intuitive conclusions from trading charts. This study uses the characteristics of deep learning to train computers in imitating this kind of intuition in the context of trading charts. The main goal of our approach is combining the time-series modeling and convolutional neural networks (CNNs) to build a trading model. We propose three steps to build the trading model. First, we preprocess the input data from quantitative data to images. Second, we use a CNN, which is a type of deep learning, to train our trading model. Third, we evaluate the model's performance in terms of the accuracy of classification. The experimental results show that if the strategy is clear enough to make the images obviously distinguishable the CNN model can predict the prices of a financial asset. Hence, our approach can help devise trading strategies and help clients automatically obtain personalized trading strategies. [ABSTRACT FROM AUTHOR]

We examine the trading strategies of mutual funds in emerging markets. We develop a method for disentangling the behavior of fund managers from that of underlying investors. For both managers and investors, we strongly reject the null hypothesis of no momentum trading: mutual funds systematically sell losers and buy winners. Selling current losers and buying current winners is stronger during crises, and equally strong for managers and investors. Selling past losers and buying past winners is stronger for managers. Managers and investors also practice contagion trading-they sell (buy) assets from one country when asset prices fall (rise) in another. (C) 2003 Elsevier B.V. All rights reserved.

In this paper, we study the statistical properties of the moneyness scaling transformation, which adjusts the moneyness coordinate of the implied volatility smile in an attempt to remove the discrepancy between the IV smiles for levered and unlevered ETF options. We construct bootstrap uniform confidence bands which indicate that the implied volatility smiles are statistically different after moneyness scaling has been performed. An empirical application shows that there are trading opportunities possible on the LETF market. A statistical arbitrage type strategy based on a dynamic semiparametric factor model is presented. This strategy presents a statistical decision algorithm which generates trade recommendations based on comparison of model and observed LETF implied volatility surface. It is shown to generate positive returns with a high probability. Extensive econometric analysis of the LETF implied volatility process is performed including out-of-sample forecasting based on a semiparametric factor model and a uniform confidence bands' study. These provide new insights into the latent dynamics of the implied volatility surface. We also incorporate Heston stochastic volatility into the moneyness scaling method for better tractability of the model. [ABSTRACT FROM AUTHOR]

Wind power producers participating in today's electricity markets face significant variability in revenue streams, with potential high losses mostly due to wind's limited predictability and the intermittent nature of the generated electricity. In order to further expand wind power generation despite such challenges, it is important to maximize its market value and move decisively towards economically sustainable and financially viable asset management. In this paper, we introduce a decision‐making framework based on stochastic optimization that allows wind power producers to hedge their position in the market by trading physically settled options in futures markets in conjunction with their participation in the short‐term electricity markets. The proposed framework relies on a series of two‐stage stochastic optimization models that identify a combined trading strategy for wind power producers actively participating in both financial and day‐ahead electricity markets. The proposed models take into consideration penalties from potential deviations between day‐ahead market offers and real‐time operation and incorporates different preferences of risk aversion, enabling a trade‐off between the expected profit and its variability. Empirical analysis based on data from the Nordic region illustrates high efficiency of the stochastic model and reveals increased revenues for both risk neutral and risk averse wind producers opting for combined strategies. [ABSTRACT FROM AUTHOR]

This paper investigates empirically whether time-series momentum returns can explain the performance of hedge funds in the cross section. Relying on the trend-following literature, a volatility-adjusted time-series momentum signal is applied on a daily basis across a large set of futures, covering the major asset classes. We build a hierarchical set of trend factors: the full version TREND can be split in summable factors across two dimensions: the horizon of the signals and the traded asset class. We show that Managed Futures, Global Macro and Fund of Hedge Funds strategies can be partly explained by a TREND exposure. Moreover, a TREND exposure is a significant determinant of hedge funds returns at the fund level, for Managed Futures and Global Macro but also, and more surprisingly, for the other styles. [ABSTRACT FROM AUTHOR]

This article considers the problem of optimal liquidation of a position in a risky security quoted in a financial market, where price evolution are risky and trades have an impact on price as well as uncertainty in the filling orders. The problem is formulated as a continuous time stochastic optimal control problem aiming at maximising a generalised risk-adjusted profit and loss function. The expression of the risk adjustment is derived from the general theory of dynamic risk measures and is selected in the class of g-conditional risk measures. The resulting theoretical framework is nonclassical since the target function depends on backward components. We show that, under a quadratic specification of the driver of a backward stochastic differential equation, it is possible to find a closed form solution and an explicit expression of the optimal liquidation policies. In this way, it is immediate to quantify the impact of risk adjustment on the profit and loss and on the expression of the optimal liquidation policies. [ABSTRACT FROM AUTHOR]

The possibility of using the belief function theory for developing of trading strategies is considered in this paper. An analysis of this approach is given on the data of the Russian stock market. The belief and plausibility functions (and their corresponding bodies of evidence) to the system's recommendations (buy, sell or hold) are calculated using fuzzy inference methods for technical indicators. Further, these bodies of evidence are aggregated using the combining rules (Dempster's rule, Yager's rule and others). The discount coefficients of the bodies of evidence are calculated at the stage of the learning under the condition of maximizing the profitability of the trading strategy. The intervals for the buying or selling of assets are determined on the results of such combination. The decision about the corresponding action is taken after comparing these intervals. The study showed that the proposed approach provides an interesting result. [ABSTRACT FROM AUTHOR]

This paper formulates and examines a new type of bivariate time series trading strategy based on signals generated from cross-country quantiles of return distributions. We conduct rolling quantile trading strategies separately in the U.S. and Chinese futures markets for soybeans, wheat, corn and sugar over very short (daily, intraday and overnight) holding periods. Overall, we find that these practical strategies outperform various benchmarks and there is a large profit potential when trades follow quantile-based signals rather than focusing on the median only. The results highlight the value of cross-country trading strategies and the harnessing of information from different parts of the return distributions which have so far been neglected. [ABSTRACT FROM AUTHOR]

This paper provides evidence that financial reporting quality (FRQ) influences the holding costs of trading strategies. While prior research has focused on the benefits of investment strategies based on poor FRQ (i.e., larger returns due to a greater amount of private information), we examine whether poor FRQ imposes greater holding costs on certain trading strategies. We show that poor FRQ motivates sophisticated investors with short-term horizons to tilt their portfolios away from value stocks, whose returns are contingent on investors revising their beliefs about firm fundamental value, and toward past winner stocks, whose future returns are realized more quickly. Poor FRQ also increases the length of time that institutions maintain large positions in value stocks. Our results imply that mis-valuations can be persistent when arbitrageurs perceive high holding costs from poor financial quality, even when they can see through the opaque financial disclosures. [ABSTRACT FROM AUTHOR]

In the last 20 years, relative value strategies have increased in popularity in various asset classes, including equity and commodity markets. Due to an increase in market participants, more sophisticated algorithms than those used in the past are now required to generate excess returns in pairs trading strategies. Sophisticated algorithms can cause an increase in complexity which, in-turn, increases computational run time. In our pairs trading example, C++ provides the best performance, however, it is also the most time consuming to implement. Among the languages that allow for faster development, Cython provides the best balance between run times and ease of prototyping. [ABSTRACT FROM AUTHOR]

We investigate the relationship between insider trading and stock returns in firms with concentrated ownership. To this end, we employ data from East Asian countries which span the period January 2003 to May 2012. Consistent with the previous literature, we find a significantly negative relation between the selling activity of insiders and stock returns. However, contrary to studies which focus on highly developed markets, we find that the buying activity of insiders is also inversely related to future stock returns. Our analysis shows that top directors with higher ownership levels drive this result, suggesting that the trading activity of insiders is not always associated with profit-making motives and can be explained by their level of ownership. Furthermore, we demonstrate that a trading strategy which focuses solely on purchases made by top directors with high ownership levels yields negative returns. The paper has important implications for outside investors who mimic the trading activity of insiders with the aim to realise profits. [ABSTRACT FROM AUTHOR]

This paper deals with efficiency of the MACD indicator. The aim of the study is to find whether this indicator would be usable for creating a business strategy for active trading at a stock exchange. The research was carried out on the basis of day closing share prices of the company CEZ, traded on the Prague stock exchange, during the ten-year period from 2006 to 2016. The MACD indicator was calculated in three slightly modified methods. The first method was based on the crossing of MACD and signal line, the second method was similar but reliant on oscillation line. The last method was based on the crossing of MACD and oscillation line. All these methods were calculated in three time periods (short-term, medium-term and long-term). None of the MACD methods beat passive strategy “buy-and-hold” in short-term period. However, for the medium-term and long-term periods, in four of six cases the MACD methods were better then the buy-and-hold strategy. Especially the third method shows very promising results. This study could be used as a building block for further research.

This study examined the profitability of technical analysis using moving-average (MA) crossover strategy compared with the conventional simple buy-and-hold strategy, using Malaysian equity market. We investigates the performance of the original moving-average strategy and a modified moving-average crossover strategy with additional trading rules such as entry rule, exit rule, holding rule, and stop-loss rule. The results are consistent to previous studies that strongly support moving-average crossover trading strategies. The result suggests that all combinations of short-MA and long-MA periods of the original MA crossover strategy and majority combinations of short-MA and long-MA of the modified MA crossover strategy outperform market benchmark with higher risk-adjusted return. In addition, the 1-period short-MA demonstrates the best return in both original and modified moving-average crossover strategy; better still the modified strategy outperforms the original strategy with lower frequency of trades which could largely reduce transaction costs and with lower return distribution variability.

Research does not indicate a consensus on the relationship between idiosyncratic volatility and asset returns. Moreover, the role of cross sectional higher order moments in predicting market returns is relatively unexplored. We show that the cross sectional volatility measure suggested by Garcia et al. is highly correlated with alternative measures of idiosyncratic volatility constructed as variance of errors from the capital asset pricing model and the Fama French model. We find that cross sectional moments help in predicting aggregate market returns in some sample countries and also provide information for portfolio formation, which is more consistent for portfolios sorted on sensitivity to cross sectional skewness.

Undiversifiable (or systematic risk) has long been an enemy of investors. Many countercyclical strategies have been developed to counter this. However, like all insurance types, these strategies are generally costly to implement, and over time can significantly reduce portfolio returns in long and extended bull markets. In this paper, we discuss an alternative technique, founded on the premise of physiological bias and risk-aversion. We take a behavioral discussion in order to contextualize the insurance like characteristics of option pricing and discuss how this can lead to a mispricing of the asymmetric relationship between the VIX and the S&P 500. To test this, we perform studies in which we find statistical inefficiencies, thereby making it possible to implement a method of hedging index option premium in a way that has displayed no monthly drawdowns in bullish periods, while still providing large returns in major sell-offs. The three versions of the strategy discussed have negative betas to the S&P 500, while exhibiting similar risk-adjusted excess returns over both bull and bear markets. Further, the performance generated over the entire period, for all three strategies, is highly statistically significant. The results challenge the weak form of the Efficient Market Hypothesis and provide evidence that the methods of hedging could be a valuable addition to an equity rich portfolio for the purpose of counteracting systematic risk.

This study hypothetically analyzes the distribution of the premiums paid and thus the subsidies received by farmers participating in the Risk Management Agency (RMA) multi-peril crop insurance program. The results show a wide spread in the effective subsidy levels, to where some producers might not be receiving any subsidies at all (i.e., they actually pay close to their full actuarially fair premium), while others only pay a small fraction of their actuarially fair premium. More importantly, the results show that "shrinkage" estimators such as the one used by the RMA have the unintended negative consequence of disproportionally subsidizing farmers who are less effective in managing risk. Producers whose farms exhibit higher downside yield variability receive much more generous subsidies than those with lower levels of yield variability.

In the mutual fund literature, it is an established fact that investors “chase past performance”. However, the opposite impact of flows on performance is widely discussed. Mainly, liquidity costs are held responsible for short-term erosion of performance, while high inflows enhance performance over longer horizons. I investigate this relation for various groups of equity, bond, and money market funds and find significant outperformance in high inflow funds over several months, especially for specific bond fund groups. In addition, I test whether this information can be exploited using simple investment strategies but find that the abnormal returns are too low to offset associated costs.

In this work, an innovative approach to exploit the true potentials of machine learning (ML) by the financial industry is presented; using ML technology not as a source of investment ideas but as a consultant for trading decisions. In particular, the artificial intelligent risk management system (AIRMS) is presented that introduces one of the first efforts in the literature to utilize supervised ML as a risk management tool. Two AIRMSs systems are developed based on two well-known ML algorithms, i.e. artificial neural networks and decision trees. These two systems are applied into the five major currency pairs (FOREX) using signals obtained from an existing technical breakout trading strategy introduced in previous study by the authors, covering a seven-year period (2010–2016). Technical indicators and times series of the past entry points feed AIRMS in order to classify produced signals from the trading strategy into two classes: profitable and not. Constructing new portfolios using signals classified only as profitable resulted in an increased profit of more than 50% compared to the original ones. In this work, technical improvements are also proposed on the application of ML algorithms to financial data related to evaluation metrics and smoothing inputs. The obtained results revealed that the two AIRMSs can achieve impressive improvements to the performance of already profitable portfolios and proved that using ML to build risk management tools is very promising. [ABSTRACT FROM AUTHOR]

We present a detailed study of the performance of a trading rule that uses moving averages of past returns to predict future returns on stock indexes. Our main goal is to link performance and the stochastic process of the traded asset. Our study reports short-, medium- and long-term effects by looking at the Sharpe ratio (SR). We calculate the Sharpe ratio of our trading rule as a function of the probability distribution function of the underlying traded asset and compare it with data. We show that if the performance is mainly due to presence of autocorrelation in the returns of the traded assets, the SR as a function of the portfolio formation period (look-back) is very different from performance due to the drift (average return). The SR shows that for look-back periods of a few months the investor is more likely to tap into autocorrelation. However, for look-back larger than few months, the drift of the asset becomes progressively more important. Finally, our empirical work reports a new long-term effect, namely oscillation of the SR and proposes a non-stationary model to account for such oscillations. [ABSTRACT FROM AUTHOR]

In this study, we investigate ordering patterns of different types of market participants in Tokyo Stock Exchange (TSE) by examining order records of the listed stocks. Firstly, we categorize the virtual servers in the trading system of TSE, each of which is linked to a single trading participant, by the ratio of cancellation and execution in the order placement as well as the number of executions at the opening of the afternoon session. Then, we analyze ordering patterns of the servers in the categories in short intervals for the top 10 highest trading volume stocks. By classifying the intervals into four cases by returns, we observe how different types of market participants submit or execute orders in the market situations. Moreover, we investigate the shares of the executed volumes for the different types of servers in the swings and roundabouts of the Nikkei 225 index, which were observed in September in 2015. The main findings of this study are as follows: Server type A, which supposedly includes non-market making proprietary traders with high-speed algorithmic strategies, executes and places orders along with the direction of the market. The shares of the execution and order volumes along with the market direction increase when the stock price moves sharply. Server type B, which presumably includes servers employing a market making strategy with high cancellation and low execution ratio, shifts its market making price ranges in the rapid price movements. We observe that passive servers in Server type B have a large share and buy at low levels in the price falls. Also, Server type B, as well as Server type A, makes profit in the price falling days and particularly, the aggressive servers in the server type make most of the profit. Server type C, which is assumed to include servers receiving orders from small investors, constantly has a large share of execution and order volume. [ABSTRACT FROM AUTHOR]

Abstract: We examine the potential gains of using exchange rate forecast models and forecast combination methods in the management of currency portfolios for three exchange rates: the euro versus the US dollar, the British pound, and the Japanese yen. We use a battery of econometric specifications to evaluate whether optimal currency portfolios implied by trading strategies based on exchange rate forecasts outperform single currencies and the equally weighted portfolio. We assess the differences in profitability of optimal currency portfolios for different types of investor preferences, two trading strategies, mean squared error‐based composite forecasts, and different forecast horizons. Our results indicate that there are clear benefits of integrating exchange rate forecasts from state‐of‐the‐art econometric models in currency portfolios. These benefits vary across investor preferences and prediction horizons but are rather similar across trading strategies. [ABSTRACT FROM AUTHOR]