This study conducts a bibliometric analysis and systematic review of cryptocurrency trading algorithms to identify existing gaps in the area. From our standpoint, this is the first study to carry out a deep analysis of price forecasts and portfolio management in cryptocurrencies in addition to analyzing the most relevant studies and authors, trend topics of the area, and identifying countries with the most published studies. During our research, we identified some gaps that can be used for further research. Currently, there are approximately 16,000 cryptocurrencies; however, in majority of the papers, the authors have only used the top 10 ranking market capitalization cryptocurrencies, leaving aside potential minor cryptocurrencies. Thus, trading strategies using Big Data can be a potential research topic, considering the greater number of emerging cryptocurrencies.
The seminal study of Meese et al. (1983) on exchange rate forecastability had a great impact on the international finance literature. The authors showed that exchange rate forecasts based on structural models are worse than a naive random walk. This result is known as the Meese--Rogoff (MR) puzzle. Although the validity of this result has been checked for many currencies, studies for the Brazilian currency are not common. In 1999, Brazil adopted the dirty floating exchange rate regime. Rossi (2013) ran an extensive study on the MR puzzle but did not analyse Brazilian data. Our goal is to run a “pseudo real-time experiment” to investigate whether forecasts based on econometric models that use the fundamentals suggested by the exchange rate monetary theory of the 80s can beat the random model for the case of the Brazilian currency. Our work has three main differences with respect to Rossi (2013). We use a bias correction technique and forecast combination in an attempt to improve the forecast accuracy of our projections. We also combine the random walk projections with the projections of the structural models to investigate if it is possible to further improve the accuracy of the random walk forecasts. However, our results are quite in line with Rossi (2013). We show that it is not difficult to beat the forecasts generated by the random walk with drift using Brazilian data, but that it is quite difficult to beat the random walk without drift. Our results suggest that it is advisable to use the random walk without drift, not only the random walk with drift, as a benchmark in exercises that claim the MR result is not valid.
Purpose The purpose of this paper is to assess whether greater participation of venture capital/private equity (VC/PE) funds in the companies’ capital structure at the moment of initial public offering (IPO) contributes to the reduction in the underpricing of their shares. Design/methodology/approach Descriptive statistics, correlation analysis, mean difference test and cross-sectional regression were used. The final sample consisted of 89 companies making IPO in Brasil Bolsa Balcão between 2007 and 2017. Findings The participation of VC/PE funds was shown to mitigate the effect of information asymmetry on managers and shareholders, thus reducing the underpricing of companies at the moment of IPO (H1). However, the expectation that a greater participation of these funds promotes further reduction in a potential underpricing (H2) was not confirmed. Research limitations/implications One can highlight the small amount of IPOs during the sampling period due to the occurrence of international and national economic crises, as well as the difficulty in obtaining information on the participation of VC/PE funds in the companies’ capital structure. Practical implications It was observed that information asymmetry had a mitigating effect from the presence of these funds in the companies, which can improve the pricing of their shares, decrease the costs and make volume captions viable for investments, in addition to giving credibility to the market information effectiveness. Originality/value This study differs from others in that it assesses not only the influence of VC/PE funds on the reduction of the underpricing of IPO shares, but also the participation of these funds in the capital of these companies.
This study maps the academic literature on Stock Price Forecasting with Long-Term Memory Artificial Neural Networks-RNA LSTM. The objective is to know if it is suitable for time series studies, especially for stock price projection. Through bibliometric analysis and systematic literature review, it is observed that 333 authors wrote on the topic between 2018 and March 2022, and the journals Expert Systems with Applications, IEEE Access, Big Data Journal and Neural Computing and Applications, published the most relevant articles. Of the 99 articles published in this period, 43 are associated with Chinese institutions, the most cited being that of Kim and Won, who studies the volatility of returns and the market capitalization of South Korean stocks. The basis of 65% of the studies is the comparison between the RNN LSTM and other artificial neural networks. The daily closing price of shares is the most analyzed type of data, and the American (21%) and Chinese (20%) stock exchanges are the most studied. 57% of the studies include improvements to existing neural network models and 42% new projection models.
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