Overreaction diamonds: precursors and aftershocks for significant price changes

Overreactions and other behavioral effects in stock prices can best be examined by adjusting for the changes in fundamentals. We perform this by subtracting the relative price changes in the net asset value (NAV) from that of market price (MP) daily for 134 406 data points of closed-end funds trading in US markets. We examine the days before and after a significant rise or fall in price deviation and MP return and find evidence of overreaction in the days after the change. Prior to a spike in deviation we find a gradual two- or three-day decline (and analogously in the other direction). Overall, there is a characteristic diamond pattern, revealing a symmetry in deviations before and after the significant change. Much of the statistical significance and the patterns disappear when the subtraction of NAV return is eliminated, suggesting that the frequent changes in fundamentals mask behavioral effects. A second study subdivides the data depending on whether the NAV or market price is responsible for the spike in the relative difference. In a majority of spikes, it is the change in market price rather than NAV that is dominant. Among those spikes for which there is little or no change in NAV, the results are similar to the overall study. Furthermore, the upward spikes are preceded by one or two days of declining market price while NAV rises slightly or is relatively unchanged. This suggests that a cause of the spike may be due to over-positioning of traders in the opposite direction in anticipation.     

Statistical arbitrage in the Black–Scholes framework

In this study, we prove the existence of statistical arbitrage opportunities in the Black–Scholes framework by considering trading strategies that consist of borrowing at the risk-free rate and taking a long position in the stock until it hits a deterministic barrier level. We derive analytical formulas for the expected value, variance and probability of loss for the discounted cumulative trading profits. The statistical arbitrage condition is derived in the Black–Scholes framework, which imposes a constraint on the Sharpe ratio of the stock. Furthermore, we verify our theoretical results via extensive Monte Carlo simulations.     

Global Liquidity,Current Account Deficit,and Exchange Rate Balance Sheet Effects in Turkey

This study investigates the current account deficit (CAD) of Turkey from the perspective of its capital account. We discuss how global liquidity conditions and monetary policies in Turkey have contributed to higher deficits through real exchange rate appreciations. We analyze the impact and consequences of exchange rate (ER) changes on the investments of non-financial firms. In the case of real ER depreciations, we find that the magnitude of the contractionary effect through balance sheets of firms with dollarized liabilities is significantly higher than the expansionary effect through trade competitiveness. We also analyze the “soft-landing” policies aimed at reducing the CAD in Turkey and estimate the rate of economic growth that must be foregone for a percentage reduction in CAD.     

Forecasting high-frequency excess stock returns via data analytics and machine learning

Borsa Istanbul introduced data analytics to present additional information about its market conditions. We examine whether this product can be utilized via various machine learning methods to predict intraday excess returns. Accordingly, these analytics provide significant prediction ratios above 50% with ideal profit ratios that can reach up to 33%. Among all the methods considered, XGBoost (logistic regression) performs better in predicting excess returns in the long-term analysis (short-term analysis). Results provide evidence for the benefits of both the analytics and the machine learning methods and raise further discussion on the semistrong market efficiency.     

Online donation behavior in nonprofit organizations: The role of congruent processing styles

With the advent of technology, donors are increasingly donating via online channels. This shift deems it necessary for nonprofit organizations to better understand the online donor behavior. As such, the purpose of this paper is twofold. First, we investigate if nonprofit organizations are perceived differently in terms of their cognitive and affective natures. Then, we examine how different consumer processing styles, i.e. cognitive and affective, interact with consumer perceptions of nonprofit cognitive/affective orientations in influencing their donation intentions. Our results indicate that consumers with a high need for cognition are more willing to donate to predominantly cognitive nonprofit organizations, while those with high need for emotion are more willing to donate to predominantly affective nonprofit organizations. Based on these results, we suggest that nonprofit organizations can garner more donations if they request funds from donors whose processing styles are congruent with the organization. Additional recommendations for future research are provided.     

Adverse selection in cryptocurrency markets

In this article we investigate the influence that information asymmetry may have on future volatility, liquidity, market toxicity, and returns within cryptocurrency markets. We use the adverse-selection component of the effective spread as a proxy for overall information asymmetry. Using order and trade data from the Bitfinex exchange, we first document statistically significant adverse-selection costs for major cryptocurrencies. Also, our results suggest that adverse-selection costs, on average, correspond to 10% of the estimated effective spread, indicating an economically significant impact of adverse-selection risk on transaction costs in cryptocurrency markets. Finally, we document that adverse-selection costs are important predictors of intraday volatility, liquidity, market toxicity, and returns.