HKUST Business School Magazine

Biz@HKUST Biz@HKUST 42 43 // Cover // Insight The predictive power of the extant models is limited because most of them focus only on highly aggregated measures (e.g. net income) and fail to account for the differential effects of other detailed financial statement line items. Furthermore, these models are largely designed to take a linear form, and cannot capture the subtle nonlinear relationships that could be very important in driving future earnings. The research shows that machine learning forecasts outperform the extant models substantially in terms of forecast accuracy. Moreover, machine learning forecasts almost completely subsume the information content in the extant models regarding future earnings changes. Finally, the new information extracted by machine learning models predicts analyst forecast errors and future stock returns, suggesting that both financial analysts and the stock market fail to make full use of the information in financial statements. Machine Learning Limitations While machine learning shows great promise in fundamental analysis and investment decision making, it is not without limitations. Compared to other applications such as image recognition and voice recognition, the application of machine learning to investment faces several distinct challenges. First, there is much more noise in stock prices than in images or voices, and this lead to a low information-to-noise ratio for many of the prediction problems on the financial markets. The low information-to-noise ratio exacerbates the risk of overfitting, and therefore may render machine learning models useless for out-of- sample predictions. Meanwhile, non-stationarity is another problem when we use machine learning models to make investment decisions. Stock markets are highly dynamic, and the innate relationships may experience significant changes due to reasons such as changing market conditions and investor preferences, and arbitrage activities by investors. In 2020, the market witnessed a significant drop in the investment return of Renaissance Technologies, a legendary hedge fund known for adopting machine learning in its investment process. 5 In a letter to its clients, Renaissance admitted that their models are trained on historical data, so the results were not surprising considering that 2020 was unusual year. Adam Taback, chief investment officer of Wells Fargo Private Wealth Management, also notes that quantitative models might have difficulty capturing useful information when markets are volatile. These examples highlight the importance of human judgment and experience in overcoming the limitations of machine learning in the investment process. Human knowledge can be useful to determine whether patterns detected by machines reflect overfitting due to noise or reflect a sensible and sustainable relationship. Humans may also make better forward-looking logical judgments or predictions with a small amount of historical data. By better isolating information from noise and taking a forward-looking approach, human intelligence is still of paramount importance in making sound investment decisions. Thus, at least in the foreseeable future, humans are unlikely to be completely replaced by machines in the investment industries. Both humans and machines have their own parts to play in the game. The best approach would be man+machine. As Paul Tudor Jones, a renowned billionaire hedge fund manager said, “No man is better than a machine, and no machine is better than a man with a machine.” 6 The aforementioned study on earnings forecasts demonstrates the promise of this approach. The authors find that machine learning forecasts compete well against, and most of the time outperform, the collective wisdom of financial analysts from thousands of contributing brokerage firms. More importantly, the simple approach of combining machine learning and analysts’ forecasts outperforms both individually. The combined forecasts are more accurate than analysts’ consensus forecasts in 29, 31, and 32 years out of the total 33 years testing period for one, two and three years ahead forecasts respectively, indicating that human analysts can substantially improve their performance by incorporating the insights from machine learning models. 5 https://www.institutionalinvestor.com/article/ b1q3fndg77d0tg/Renaissance-s-Medallion-Fund-Surged- 76-in-2020-But-Funds-Open-to-Outsiders-Tanked 6 https://dealbreaker.com/2017/05/paul-tudor-jones- throwing-money-letters-ai How Data Analytics Are Used in Marketing A closer look into three main kinds of data-driven marketing analytics – descriptive, predictive and prescriptive. Assistant Professor LIU Jia Department of Marketing, HKUST Business School Data-driven marketing simply put means using quantitative methods to derive meaning from data to make informed marketing decisions. Thanks to the availability of a massive amount of data, data analytics in marketing has become more important than ever. According to a BMO Capital Markets report, marketers spend US$50 billion per year on big data and advanced analytics to improve marketing’s impact on business. Research by McKinsey 1 shows companies that invest in big data and analytics yield a five to six percent average increase in profits, which jumps to nine percent for investments spanning five years. In many cases, companies have focused on more open-ended efforts to gain novel insights from big data. These efforts were fuelled by analytics vendors and data scientists who were eager to take data and run all types of analyses in the hope of finding diamonds. Depending on the stage of the workflow and the requirement of data analysis, there are three main kinds of data driven marketing analytics – descriptive, predictive and prescriptive. Their underlying techniques include, but are not limited to, statistical modelling, data mining, machine learning, and AI. In this article, I will present some applications of each of the three analytics using recent research in marketing, most of which reflect my own (working) papers on marketing analytics. Descriptive Analytics Descriptive analytics refers to the interpretation of historical data to identify trends and patterns, answering the question “What has happened?”. This means descriptive analytics can help identify potential problems or future opportunities for business. In my collaboration with the Microsoft Bing search engine 2 , we have developed an interpretable machine learning model that can track, quantify, and interpret users’ topical preferences underlying each search query and across search contexts (e.g., time, location, and demographics). The proposed model leverages data on user queries, subsequent click-through on search results, and all textual information