RAM AI's RESEARCH
RAM AI’s Research drives our investment decisions and performance.
RAM AI’s researchers relentlessly explore new ways of extracting information from data to uncover new sources of return, increase diversification and improve liquidity. Our conviction is that ongoing research and a disciplined approach to investment help take advantage of market inefficiencies in a continuous manner, technological advancements being leveraged to reveal the predictive power of the exponentially-growing volume of information at our disposal.
ESG2Risk: A Deep Learning Framework for ESG
Genetic Algorithm: A Heuristic Approach to Multi-Dimensional Problems
Natural Language Processing: Unlocking the Secretas in Semantics
Carbon Offset Market Review
Machine Learning: Financial data takes a new dimension
RAM's Systematic Equity: A Leading Approach to ESG Integration
To invest or not to invest? The effect of Capex announcements
To invest or not to invest? Capitalizing R&D Expenses to increase Valuation Accuracy
A Deep Learning Framework for Climate Responsible Investment
TO INVEST OR NOT TO INVEST? CAPITALIZING R&D EXPENSES TO INCREASE VALUATION ACCURACY
The profits a firm generates can either be distributed to its shareholders through dividends/share buybacks, pay back some of its debt or be used to invest. A company invests in its future organic growth either through Research and Development (R&D) or by engaging in Capital Expenditures (Capex). In a previous research paper, we studied the effect of Capex announcements on stock returns. The present document analyses R&D activity and its impact on company fundamentals. Read full paper here.
TO INVEST OR NOT TO INVEST? THE EFFECT OF CAPEX ANNOUNCEMENTS
The profits a firm generates can either be distributed to its shareholders through dividends/share buybacks, reduce its debt or be used to invest. A company invests in its future organic growth either through Research and Development or by engaging in Capital Expenditures (Capex). As of January 2021, the net Capital Expenditure on sales ratio reached 2.54% for US companies. Read full paper here.
A DEEP LEARNING FRAMEWORK FOR CLIMATE RESPONSIBLE INVESTMENT
Incorporating climate considerations into portfolio analysis and systematic investments has drawn numerous attention recently. It is motivated by the pursuit of sustainable investing for a low-carbon transition. In this paper, we propose to integrate both structured and unstructured climate-related data into quantitative investing for stock markets, e.g. carbon emission scores and climate events from news flows. We develop a deep learning framework to consume these data for assessing climate-related opportunities and the risk of stocks in the investing universe. Experimental evaluation on real data demonstrates the low-carbon intensity of the constructed portfolio as well as decent investing return. Read full paper here.
A LEADING APPROACH TO ESG INTEGRATION
The rapid growth of ESG data in recent years makes it a compelling new dimension to the investment process, but integrating ESG presents a wide array of challenges, from noise filtering to hidden biases. In this paper we introduce some of the shortcomings of ESG data, as well as the value-added ESG inputs can bring into a stock selection process. Read full paper here.
CARBON OFFSET MARKET REVIEW
Carbon Credit is a generic term for any tradable certificate representing a certain amount of carbon emissions. A government, corporate or any individual wanting to offset a defined amount of carbon emitted by their activities, can buy credits for a specific amount of CO² to balance their emissions. Read full paper here.
Machine Learning : Financial Data Takes a New Dimension
In this paper we will look at the growing popularity of artificial intelligence and the potential for finance to prosper from machine learning implementations. As technology continues to push the boundaries of our imagination, new dimensions will undoubtedly emerge over time. Read full paper here.
UNLOCKING THE SECRETS IN SEMANTICS
Traditional market and factor datasets are typically structured in numerical terms in a form that can be digested by statistical models. Machine Learning can help deal with the abundance of rich textual data gleaned from financial news, earnings reports, and transcripts and their correlation to markets, currently, less exploited by quantitative managers. We take a dive into NLP techniques and their integration methods at RAM AI in the following Q&A. Read Q&A with RAM’s Data Scientist, Tian Guo.
PREDICTING THE IMPACT OF ESG NEWS WITH DEEP LEARNING – ESG2RISK
Incorporating environmental, social, and governance (ESG) considerations into systematic investments have drawn numerous attention recently. In this paper, we focus on the ESG events in financial news flow and explore the predictive power of ESG related financial news on stock volatility. In particular, we develop a pipeline of ESG news extraction, news representations, and Bayesian inference of deep learning models. Experimental evaluation on real data and different markets demonstrates the superior predicting performance as well as the relation of high volatility prediction to stocks with potentially high risk and low return. It also shows the prospect of the proposed pipeline as a flexible predicting framework for various textual data and target variables. Read full paper here.
GENETIC ALGORITHMS: A HEURISTIC APPROACH TO MULTI-DIMENSIONAL PROBLEMS
Evolutionary algorithms are not new and have been developed, both their concepts and framework, since around the 1950s based on the idea that the evolutionary process could be used as a general-purpose optimization tool. Read full paper here.