Forecasting the Market with Machine Learning Algorithms: An Application of NMC-BERT-LSTM-DQN-X Algorithm in Quantitative Trading

Although machine learning (ML) algorithms have been widely used in forecasting the trend of stock market indices, they failed to consider the following crucial aspects for market forecasting: (1) that investors’ emotions and attitudes toward future market trends have material impacts on market trend forecasting (2) the length of past market data should be dynamically adjusted according to the market status and (3) the transition of market statutes should be considered when forecasting market trends. In this study, we proposed an innovative ML method to forecast China's stock market trends by addressing the three issues above. Specifically, sentimental factors (see Appendix [1] for full trans) were first collected to measure investors’ emotions and attitudes. Then, a non-stationary Markov chain (NMC) model was used to capture dynamic transitions of market statutes. We choose the state-of-the-art (SOTA) method, namely, Bidirectional Encoder Representations from Transformers ( BERT ), to predict the state of the market at time t , and a long short-term memory ( LSTM ) model was used to estimate the varying length of past market data in market trend prediction, where the input of LSTM (the state of the market at time t ) was the output of BERT and probabilities for opening and closing of the gates in the LSTM model were based on outputs of the NMC model. Finally, the optimum parameters of the proposed algorithm were calculated using a reinforced learning-based deep Q-Network. Compared to existing forecasting methods, the proposed algorithm achieves better results with a forecasting accuracy of 61.77%, annualized return of 29.25%, and maximum losses of −8.29%. Furthermore, the proposed model achieved the lowest forecasting error: mean square error (0.095), root mean square error (0.0739), mean absolute error (0.104), and mean absolute percent error (15.1%). As a result, the proposed market forecasting model can help investors obtain more accurate market forecast information.

Perspectives on Salmon Aquaculture: Current Status, Challenges and Genetic Improvement for Future Growth

With an estimated global value of US$15.6 billion, farmed salmonids represent a precious food resource, which is also the fastest increasing food producing industry with annual growth of 7% in production. A total average of 3,594,000 metric tonnes was produced in 2020, behind Chinese and Indian carps, tilapias and catfishes. Lead producers of farmed salmonids are Norway, Chile, Faroe, Canada and Scotland, stimulated by increasing global demand and market. However, over the last 2 years, production has been declining, occasioned by effects of diseases as well as rising feed costs. Over the last year, production has declined sharply due to effects of covid-19. This chapter reviews the species in culture, systems of culture, environmental footprints of salmon culture, and market trends in salmon culture. Burden of diseases, especially Infectious pancreatic Necrosis, Infectious salmon anemia and furunculosis, as well as high cost of feed formulation, key challenges curtailing growth of the salmon production industry, are discussed. A review is made of the international salmon genome sequencing effort, selective breeding for disease resistance, and the use of genomics to mitigate challenges of diseases that stifle higher production of salmonids globally.

A Critical Review of Biofuels as an Alternative Fuel to Diesel in Gulf Regional Corporation (GCC) Region: Current Market Trends and Future Opportunitie

Bio Fuels are considered as good alternatives for conventional fossil fuels. By the year 2020, in the GCC region these fuels are able to meet around 0.5 - 1% of total transportation fuel demand. This industry grew at rate of 3.4% over the period of 2015-2020 with a strong projected growth in the Kingdom of Saudi Arabia (KSA). Bio fuels are used to operate automotive for mining and construction industries. Critical barriers in foreign investments pose a major challenge for growth of this sector in the GCC region. The presented work discusses situation and growth predictions of Bio-Fuel industry in the GCC region. It also discusses about current growth, trends, opportunities and challenges being faced by major companies operating in the GCC region.

Environmental, Social, and Governance Assets

This chapter presents a historical overview of the emergence and evolution of ESG assets and, in particular, analyses the main market trends that have been observed in recent years in relation to these assets. The authors intend to present a summary of the main moments and phases that these assets have gone through, from the moment of their appearance in 2007, the year in which the European Investment Bank carried out its Climate Awareness Bond as a test issuance. The movement associated with the issue of these assets is initiated by supranational entities with little homogeneity and no fixed conventions. To overcome this impasse, the green bond principles emerged and a process of defining the characteristics of these assets began, with a particular focus on transparency and the governance process. From this stage onwards, the market showed interest in these financial products and hence the emergence of a harmonising movement regarding green bond standards in which Europe seems to have taken a leading role.

Open RAN—Radio Access Network Evolution, Benefits and Market Trends

Open RAN (radio access network) movement is perceived as a game changer, having robust potential to introduce shifts in mobile radio access networks towards tailor-made solutions based on the architecture decomposition. It is widely assumed that those changes will affect the approach to network deployments and supply chains of network elements and their further integration and maintenance. First deployments of O-RAN-based networks have already delivered broadband services to end users. In parallel, many proof-of-concept feature evaluations and theoretical studies are being conducted by academia and the industry. In this review, the authors describe the RAN evolution towards open models and make an attempt to indicate potential open RAN benefits and market trends.

Reflecting on the Effects of Disruptive Innovation in South Africa’s Accommodation Subsector: A Focus on Emerging Black-Owned Businesses

South Africa’s tourism industry has experienced significant changes since 1994. These changes have been propelled by the government’s developmental policies with the aim being to redress the imbalances of past racial injustices. The tourism and hospitality industries remain dominated by white-owned enterprises as well as by large and well-established international brands. In light of this narrative, this paper reflects on how the ever-growing online dominion offers palpable opportunities to deal with the ongoing struggles faced by existing and emerging black-owned small tourism businesses in South Africa. Specifically, the paper reviews key literature on disruptive innovation in the sharing accommodation economy. An important take from the literature is that for emerging black-owned accommodation ventures to succeed in the digital era, there needs to be a strategic shift in how they understand new market trends in the industry. The latter is imperative for their success and sustainability in a post-pandemic world.

A Novel Trading Strategy Framework Based on Reinforcement Deep Learning for Financial Market Predictions

The prediction of stocks is complicated by the dynamic, complex, and chaotic environment of the stock market. Investors put their money into the financial market, hoping to maximize profits by understanding market trends and designing trading strategies at the entry and exit points. Most studies propose machine learning models to predict stock prices. However, constructing trading strategies is helpful for traders to avoid making mistakes and losing money. We propose an automatic trading framework using LSTM combined with deep Q-learning to determine the trading signal and the size of the trading position. This is more sophisticated than traditional price prediction models. This study used price data from the Taiwan stock market, including daily opening price, closing price, highest price, lowest price, and trading volume. The profitability of the system was evaluated using a combination of different states of different stocks. The profitability of the proposed system was positive after a long period of testing, which means that the system performed well in predicting the rise and fall of stocks.

Evaluating the Impact of the Accounting Doctoral Scholars Program on Academic Accounting in the US

The accounting establishment and AICPA Foundation responded to an inadequate supply of new accounting faculty by creating the Accounting Doctoral Scholars (ADS) program. Between 2009–2018, the $17 million program enabled 105 practitioners to become audit and tax faculty. Based on market data and an ADS participant survey, we find an increase in doctoral graduates at ADS and non-ADS schools relative to pre-ADS years, and unmet demand for audit has decreased after ADS, whereas tax remains in need. Compared to the market, ADS graduates experienced somewhat better placements by moving up to more prestigious strata and were more likely to place at schools with a doctoral program. Additionally, we present results for ADS students’ motivations, degree completion time, and differences between audit and tax participants. Our findings have important implications for academic accounting, business schools, regulators, and policymakers. We also provide important context for changes in market trends preceding COVID 19.

Analysis of key trends and ways to improve the marketing activities of domestic manufacturers of municipal equipment

The results of the marketing analysis of the market of special equipment for housing and communal services of Russia are presented. Market trends have been analysed, the sales dynamics of municipal machinery have been characterised and segments of the product portfolio of domestic manufacturers have been identified. The article analyzes the features of the development of two key product segments - the segment of snowplows and snow loaders, as well as the segment of garbage trucks. It is proved that they have a high market potential and objective prerequisites for import substitution, but they need to improve marketing support. The authors proposed and justified a set of measures to improve marketing activities, including development of financial instruments to support customers, expansion of the product line, improvement of distribution strategies, optimisation of call centers and the development of exports using state support tools.