Short Term Firm-Specific Stock Forecasting with BDI Framework

Ahmed, Mansoor; Sriram, Anirudh; Singh, Sanjay

doi:10.1007/s10614-019-09911-0

Cited by 7 publications

(5 citation statements)

References 19 publications

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“…Moreover, [20] evaluated various ML algorithms for money laundering detection. Another work published by [21] analyzed the role of market sentiment and technical indicators in conjunction with ML techniques to predict stock trends.…”

Section: Related Workmentioning

confidence: 99%

Predictive Market Making via Machine Learning

et al. 2022

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Market making (MM) is an important means of providing liquidity to the stock markets. Recent research suggests that reinforcement learning (RL) can improve MM significantly in terms of returns. In the latest work on RL-based MM, the reward is a function of equity returns, calculated based on its current price, and the inventory of MM agent. As a result, the agent’s return is maximised and liquidity is provided. If the price movement is known and this information is optimally utilised, there is potential that the MM agent’s return can be further improved. Important questions are, how to predict stock price movement, and how to utilise such prediction? In this paper, we introduce the concept of predictive market marking (PMM) and present our method for PMM, which comprises a RL-based MM agent and a deep neural network (DNN)-based price predictor. A key component of PMM is the consolidated price equation (CPE), which amalgamates an equity’s present and predicted market prices into a consolidated price, which is used to generate ask and bid quotes that reflect both current price and future movement. Our PMM method is evaluated against the state-of-the-art (RL-based MM) and a traditional MM method, using ten stocks and three exchange traded funds (ETFs). Out-of-sample backtesting showed that our PMM method outperformed the two benchmark methods.

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Section: Related Workmentioning

confidence: 99%

Predictive Market Making via Machine Learning

et al. 2022

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“…If some banks have different preferences regarding, for example, interest rates, their agents would first calculate their preferences and then start the negotiation process with other agents, where they must consider the extent of the differences between their preferences. The learning methods that can be applied by each bank are not specified at this level but can include a range of machine learning methods such as supervised, unsupervised, and reinforcement learning [39][40][41]. Combining these learning methods with the BDI architecture would lead to better decisions by market members [42].…”

Section: Agent-based Modelmentioning

confidence: 99%

An Agent-Oriented, Blockchain-Based Design of the Interbank Money Market Trading System

Alaeddini

Dugdale

Reaidy

et al. 2021

Agents and Multi-Agent Systems: Technologies and Applications 2021

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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“…Five major ML algorithms, namely Bayes logistic regression, decision tree, RF, support vector machine, and artificial neural network are used in the paper. Ahmed et al (2019) investigate predicting stock trends over the short term for a specific company. To train, test and validate the system, a dataset stretching over a duration of ten years is used.…”

Section: Related Workmentioning

confidence: 99%

Inflation and Inflation Uncertainty in Growth Model of Barro: An Application of Random Forest Model

Senoussi¹

2021

International Econometric Review

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One of the major problems of the empirical economists while building an economic model is the selection of variables which should be included in the true regression model. Conventional econometrics use several model selection criteria to determine the variables. Recent years' developments in Machine Learning (ML) approaches introduced an alternative way to select variables. In this paper, I have an application of ML to select variables to include for a nonlinear relationship between inflation and economic growth. Among ML methodologies, Random Forest (RF; Breiman, 2001) approach is one of the most powerful to capture nonlinear relationships. Therefore, I applied RF and found that both high and low inflation can be the cause of low economic growth which is a major contribution of the paper to economic literature. This observation produces clear suggestions for central bank inflation targeting policies. Moreover, in the paper, as an outcome of RF there are other variables effecting economic growth with an order of importance.

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Short Term Firm-Specific Stock Forecasting with BDI Framework

Cited by 7 publications

References 19 publications

Predictive Market Making via Machine Learning

Predictive Market Making via Machine Learning

An Agent-Oriented, Blockchain-Based Design of the Interbank Money Market Trading System

Inflation and Inflation Uncertainty in Growth Model of Barro: An Application of Random Forest Model

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