Human judgments are systematically affected by various biases and distortions. The main goal of our study is to analyze the effects of five well-documented behavioral biases—namely, the disposition effect, herd behavior, availability heuristic, gambler’s fallacy and hot hand fallacy—on the mechanisms of stock market decision making and, in particular, the correlations between the magnitudes of the biases in the cross-section of market investors. Employing an extensive online survey, we demonstrate that, on average, active capital market investors exhibit moderate degrees of behavioral biases. We then calculate the cross-sectional correlation coefficients between the biases and find that all of them are positive and highly significant for both professional and non-professional investors and for all categories of investors, as classified by their experience levels, genders, and ages. This finding suggests that an investor who is more inclined to employ a certain intuitive decision-making technique will most likely accept other techniques as well. Furthermore, we determine that the correlation coefficients between the biases are higher for more experienced investors and male investors, indicating that these categories of investors are likely to behave more consistently, or, in other words, are more likely to decide for themselves whether to rely on simplifying decision-making techniques in general or to reject all of them. Alternatively, this finding may suggest that these investors develop more sophisticated “adaptive toolboxes”, or collections of heuristics, and apply them more systematically
This research has examined the ability of two forecasting methods to forecast Bitcoin’s price trends. The research is based on Bitcoin—USA dollar prices from the beginning of 2012 until the end of March 2020. Such a long period of time that includes volatile periods with strong up and downtrends introduces challenges to any forecasting system. We use particle swarm optimization to find the best forecasting combinations of setups. Results show that Bitcoin’s price changes do not follow the “Random Walk” efficient market hypothesis and that both Darvas Box and Linear Regression techniques can help traders to predict the bitcoin’s price trends. We also find that both methodologies work better predicting an uptrend than a downtrend. The best setup for the Darvas Box strategy is six days of formation. A Darvas box uptrend signal was found efficient predicting four sequential daily returns while a downtrend signal faded after two days on average. The best setup for the Linear Regression model is 42 days with 1 standard deviation.
Artificial Intelligence (AI) has been recently recognized as an essential aid for human traders. The advantages of the AI systems over human traders are that they can analyze an extensive data set from different sources in a fraction of a second and perform actual high-frequency trading (HFT) that can take advantage of market anomalies and price differences. This paper reviews the most important papers published in recent years that use the most advanced techniques to forecast financial asset trends and answer the question of whether those techniques can be used to successfully trade the complex financial markets. All systems use deep learning (DL) and machine learning (ML) protocols to explore nonobvious correlations and phenomena that influence the probability of trading success. Their predictions are based on linear or nonlinear models often combined with social media investors’ sentiment derivations or pattern recognitions. Most of the reviewed papers have proven the successful ability of their developed system to trade the financial markets.
This survey-based research deals with sectorial differences in terms of three main corporate finance policies: investment, financing and dividend. We used a multinational survey that was distributed to the chief financial officers in five countries: the US, the UK, Germany, Canada and Japan. We found statistically significant differences between the nine sectors examined in terms of all the three major financial policies. These differences may be due to the following: (1) the unique financial needs and operating conditions of each sector and (2) the imitation effect according to which firms imitate the financial behavior of other firms in their sector. We found that the use of established investment appraisal techniques is most common in the construction sector and least common in the technology sector. The IRR is the most frequently used investment appraisal technique for the entire survey sample, especially in the communication sector; however, it is rarely used in the technology sector. The technology sector has the lowest level of financial leveraging, while the finance sector has the highest level. A constant sum per share is the most common dividend policy in the following sectors: retail and wholesale, services, manufacturing and transport. On the other hand, construction, energy, communication and technology sectors are characterized by a high percentage of firms that do not pay dividends at all.investment policy, financing policy, dividend policy, corporate finance, sectorial differences, multinational survey,
In this study we use a sample of 334 S&P500 companies to examine the extent to which financially distressed firms pay dividends in order to attract investors. We find a higher dividend yield and a higher pay-out ratio for financially distressed firms than for financially stable firms. We also find that financially distressed firms tend to change the dividend per share more rapidly than stable firms. Furthermore, these firms' dividends depend more on earnings than do the dividends of stable companies. This finding is consistent with the frequent dividend changes observed in distressed firms. Stable firms, in contrast, prefer paying dividends that are less dependent upon earnings. These results may stem from the relatively high level of importance that financially distressed firms ascribe to dividend payments or to the aggressive dividend policy that eroded the firms' financial stability and forced them to reduce the dividend per share rapidly.
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