Frontrunning the signals: As arbitrage between sophisticates

This paper presents a model in which some sophisticated investors do not wait for receipt of a signal before purchasing an asset. Its critical innovation is an arbitrage equation for frontrunning. Some sophisticates who will receive information in the next period arbitrage against similar sophisticates who will act on that information in that next period when the information is received. The costs of such frontrunning are borne totally by unsophisticated traders—with no gain or loss to sophisticates. Nor does the frontrunning produce any information discovery. Thus, this paper describes a financial-market anomaly: of inefficient financial transactions with gains to no one.

Design of data mining algorithm based on rough entropy for us stock market abnormality

The economic interaction between the countries of the world is gradually strengthening. Among them, the US stock market is a “barometer” of the global economy, which has a huge impact on the global economy. Therefore, it is of great significance to study the data in the US stock market, especially the data mining algorithm of abnormal data. At present, although data mining technology has achieved many research results in the financial field, it has not formed a good research system for time series data in stock market anomalies. According to the actual performance and data characteristics of the stock market anomaly, this paper uses data mining techniques to find the abnormal data in the stock market data, and uses the isolated point detection method based on density and distance to analyze the obtained abnormal data to obtain its implicit useful information. However, due to the defects of traditional data mining algorithms in dealing with stock market anomalies containing uncertain factors, that is, the errors caused by other human factors, this paper introduces the roughening entropy of the uncertainty data and applies its theory to the field of data mining, a data mining algorithm based on rough entropy in the US stock market anomaly is designed. Finally, the empirical analysis of the algorithm is carried out. The experimental results show that the data mining algorithm based on rough entropy proposed in this paper can effectively detect the abnormal fluctuation of time series in the stock market.

Studi Komparatif The Day Of The Week Effectmdan The Month Of The Year Effect Dalam Perolehan Return Saham Di Bursa Efek Indonesia Pada Perusahaan LQ 45 Periode Februari 2017-Januari 2018

The concept of the efficient capital markets has become a topic of debate is fascinating and quite controversial in the field of finance. Since the introduction his the efficient market hypothesis, comes a variety of behavior of irregularity or discrepancy in the capital markets. Irregularity is referred to as a market anomaly (market anomaly). The Market anomaly that became a lot of attention is the anomalous effect of calendar. These anomalies are the day of the week effect and the month of the year effect. This research was conducted due to the results of several studies that are not consistent on the day of the week effect and the month of the year effect in obtaining the return of shares in Indonesia stock exchange.

DOES HEURISTIC BEHAVIOR LEAVE ANOMALIES IN THE CAPITAL MARKET?

Introduction/Main Objectives: This study aims to examine the role of heuristics behavior towards the formation of fundamental and technical anomalies in the capital market. Besides, this study also aims to examine the role of fundamental and technical anomalies on investment performance. Background Problems: EMH is not always able to explain all events or phenomena so that it still raises questions and gives results from research that do not meet the expectations, and in the end, this phenomenon is categorized as a market anomaly. This study investigates whether heuristics have an effect on fundamental and technical anomalies and whether the anomalies have an effect on investment performance. Novelty: There is no research that uses hindsight variables incorporated into heuristics; therefore, this study confirms that the indicators used in hindsight measurements are appropriate for measuring what will be measured. Previous research did not involve hindsight in the heuristic category. Research Methods: Data management is done by using SEM with the help of the Warp-PLS analysis tool. Mediation exploration testing with the VAF (Variance Accounted For). Findings/Results: The results of the study show that heuristics (availability, representativeness, and hindsight) are proven to be one of the factors that cause fundamental and technical anomalies in the capital market except for availability heuristics. Conclusion: A large number of anomalies in the capital market do not stop investors from continuing to invest so that at a certain level of investors are satisfied with their investment performance because they use heuristics in an efficient way.

ANOMALI OVERREACTION HYPOTHESIS DI BURSA EFEK INDONESIA

Penelitian ini melakukan pengujian terhadap overreaction hypothesis, dalam hipotesis ini disebutkan bahwa portofolio loser setelah perioda pengujian akan memiliki rata-rata abnormal return kumulatif yang lebih baik, sedangkan portofolio winner setelah perioda pengujian akan mengalami pembalikan return, dalam hal ini rata-rata abnormal return kumulatif return portofolio winner akan menunjukkan performance yang semakin memburuk.Fenomena pembalikan return ini dikenal dengan anomali winner-losser.Penelitian ini dengan menggunakan data saham yang terdaftar dalam LQ45 selama perioda 2016-2018, penelitian ini menemukan fenomena pembalikan return secara random, fenomena pembalikan return pada setiap minggu ke 4 secara acak membuktikan bahwa overreaction hypothesis terbukti.Hal ini membuktokan adanya fenomena anomali winner losser di passar modal Indonesia selama perioda 2016-2018.Keywords : overreaction hypothesis, contrarian strategy, market anomaly, portofolio Losser,portofolio winner

Investor perception of fair value evaluation: focusing on financial instruments

This study analyzed capital market investors’ recognition of the predictability of fair value-based valuation. It was examined if market investors overvalue the predictive value of fair value by comparing that value with that measured in accounting performance. The results reveal that investors are likely to overvalue fair value more than predictive values reflected in accounting performance. In particular, the results show that investors can gain abnormal returns through the market anomaly due to the functional fixation that investors cannot distinguish between unrealized profits and realized ones. Though there are considerable studies about accrual anomaly, few studies explore it with the separation of unrealized profits from total accruals. A number of studies about the causes of accrual anomaly have been conducted from various perspectives. The analysis of this study argues that the unrealized profits derived from fair value evaluation can be a cause of accrual anomaly. On the basis of the result, this study suggests that information about unrealized earnings should be reported separately.