Frequency Effects on Predictability of Stock Returns

We propose that predictability is a prerequisite for profitability on financial markets. We look at ways to measure predictability of price changes using information theoretic approach and employ them on all historical data available for NYSE 100 stocks. This allows us to determine whether frequency of sampling price changes affects the predictability of those. We also relations between price changes predictability and the deviation of the price formation processes from iid as well as the stock's sector. We also briefly comment on the complicated relationship between predictability of price changes and the profitability of algorithmic trading.Comment: 8 pages, 16 figures, submitted for possible publication to Computational Intelligence for Financial Engineering and Economics 2014 conferenc

Stock market predictability : non-synchronous trading or inefficient markets? Evidence from the National Stock Exchange of India

Purpose: The main objective of this study is to obtain new empirical evidence on non-synchronous trading effects through modelling the predictability of market indices. Design / Methodology / Approach: We test for lead-lag effects between the Indian Nifty and Nifty Junior indices using Pesaran-Timmermann tests and Granger-Causality. We then propose a simple test on overnight returns, in order to infer whether the observed predictability is mainly attributable to non-synchronous trading or some form of inefficiency. Findings: The evidence suggests that non-synchronous trading is a better explanation for the observed predictability in the Indian stock market. Research limitations / implications: The indication that non-synchronous trading effects become more pronounced in high-frequency data, suggests that prior studies using daily data may underestimate the impacts of non-synchronicity. Originality / value: The originality of the paper rests on various important contributions: (a) we look at overnight returns to infer whether predictability is more attributable to non-synchronous trading or to some form of inefficiency, (b) we investigate the impacts of non-synchronicity in terms of lead-lag effects rather than serial correlation, and (c) we use high-frequency data which gauges the impacts of non-synchronicity during less active parts of the trading day.peer-reviewe

Are technical indicators helpful to investors in china’s stock market? A study based on some distribution forecast models and their combinations

Can investors use technical analysis to generate positive riskadjusted returns by observing historical transaction data? The study investigates whether technical indicators (TIs) are beneficial to the returns and risk management of China’s stock market investors. It is conducted from the perspective of a distribution forecast rather than a traditional point forecast. The study investigates the TIs’ predictability on the distribution of returns. It also examines the TIs’ impact on risk management. A high-dimensional-same-frequency information distribution forecasting model, the LASSO-EGARCH model, is built. The LASSO regression’s results show that the TIs have limited ‘explanatory power’ for the return prediction. However, the adaptive moving average and turnover rate show significant and robust effects. The statistical evaluation and economic evaluation show that the TIs information’s integration cannot improve the direction forecast’s accuracy, nor does it have excess profitability. However, it enables the return distribution to have a better calibration. The above conclusion reveals that the usefulness of the analysis for China’s stock market lies in its risk management when the stock price plunges, rather than in excess profits. This may provide a reference for investors who prefer the TIs’ analysis

Essays in Empirical Macroeconomics

In the first chapter I analyze the predictability of European stock returns, using a large set of stock-level predictors and several machine learning algorithms. The analysis suggests monthly returns are hardly predictable. In the second and third chapters monetary policy in the Euro Area is studied in a core-periphery perspective. First, I study the effects of the quantitative easing on the convenience yield on safe German bonds. I identify a contractionary component of the QE related to the induced increase in the scarcity of German bonds. In the last chapter I identify a novel shock, necessary to fully characterize monetary policy in the Euro Area, using high-frequency variations of asset prices around ECB press conferences. This shock generates from the ECB having a direct role in driving expectations about the credit/redenomination risk of peripheral countries’ debt and have tangible effects on Euro Area economy

Intraday Patterns in the Cross-section of Stock Returns

Motivated by the literature on investment flows and optimal trading, we examine intraday predictability in the cross-section of stock returns. We find a striking pattern of return continuation at half-hour intervals that are exact multiples of a trading day, and this effect lasts for at least 40 trading days. Volume, order imbalance, volatility, and bid-ask spreads exhibit similar patterns, but do not explain the return patterns. We also show that short-term return reversal is driven by temporary liquidity imbalances lasting less than an hour and bid-ask bounce. Timing trades can reduce execution costs by the equivalent of the effective spread

Financial asset returns, direction-of-change forecasting, and volatility dynamics

We consider three sets of phenomena that feature prominently - and separately - in the financial economics literature: conditional mean dependence (or lack thereof) in asset returns, dependence (and hence forecastability) in asset return signs, and dependence (and hence forecastability) in asset return volatilities. We show that they are very much interrelated, and we explore the relationships in detail. Among other things, we show that: (a) Volatility dependence produces sign dependence, so long as expected returns are nonzero, so that one should expect sign dependence, given the overwhelming evidence of volatility dependence; (b) The standard finding of little or no conditional mean dependence is entirely consistent with a significant degree of sign dependence and volatility dependence; (c) Sign dependence is not likely to be found via analysis of sign autocorrelations, runs tests, or traditional market timing tests, because of the special nonlinear nature of sign dependence; (d) Sign dependence is not likely to be found in very high-frequency (e.g., daily) or very low-frequency (e.g., annual) returns; instead, it is more likely to be found at intermediate return horizons; (e) Sign dependence is very much present in actual U.S. equity returns, and its properties match closely our theoretical predictions; (f) The link between volatility forecastability and sign forecastability remains intact in conditionally non-Gaussian environments, as for example with time-varying conditional skewness and/or kurtosis

An Analysis of the Impacts of Non-Synchronous Trading On

The serial correlation effects which non-synchronous trading can induce in financial data have been documented by various researchers. In this paper we investigate non-synchronous trading effects in terms of the predictability that may be induced in the values of stock indices. This analysis is applied to emerging-market data, on the grounds that such markets might be less liquid and thus prone to a higher degree of non- synchronous trading. We use both a daily data set and a higher frequency one, since the latter is a prerequisite for capturing intra-day variations in trading activity. When considering one-minute interval data, we obtain clear evidence of predictability between indices with different degrees of non-synchronous trading. We then propose a simple test to infer whether such predictability is mainly attributable to non- synchronous trading or an actual delayed adjustment on part of traders. The results obtained from an intra-day analysis suggest that the former cause seems a better explanation for the observed predictability. Future research in this area is needed to shed light on the degree of data predictability which may be exclusively attributed to non-synchronous trading, and how empirical results may be influenced by the chosen data frequency.Non-Synchronous Trading, Stock Markets, National Stock Exchange of India, High-Frequency Data.

Stock predictability and preceding stock price changes - Evidence from central and Eastern European markets

This paper extends the empirical evidence on stock returns after preceding price innovations using data from Central and Eastern European (CEE) markets. In contrast to many previous papers, we find no evidence of either overreaction effects or rational adjustments to increased risk after large preceding price movements. We do, however, see strong evidence of trends in the data with price falls(rises) of all sizes being followed by subsequent price falls(rises)

Maximum Entropy Production Principle for Stock Returns

In our previous studies we have investigated the structural complexity of time series describing stock returns on New York's and Warsaw's stock exchanges, by employing two estimators of Shannon's entropy rate based on Lempel-Ziv and Context Tree Weighting algorithms, which were originally used for data compression. Such structural complexity of the time series describing logarithmic stock returns can be used as a measure of the inherent (model-free) predictability of the underlying price formation processes, testing the Efficient-Market Hypothesis in practice. We have also correlated the estimated predictability with the profitability of standard trading algorithms, and found that these do not use the structure inherent in the stock returns to any significant degree. To find a way to use the structural complexity of the stock returns for the purpose of predictions we propose the Maximum Entropy Production Principle as applied to stock returns, and test it on the two mentioned markets, inquiring into whether it is possible to enhance prediction of stock returns based on the structural complexity of these and the mentioned principle.Comment: 14 pages, 5 figure