HKUST Business School Magazine

Biz@HKUST Biz@HKUST 40 41 // Cover // Insight A Man with a Machine The investment industry is increasingly using AI technology to make investment decisions, but computers are not ready to replace human intelligence just yet. Professor YOU Haifeng Department of Accounting, HKUST Business School Artificial intelligence (AI), which is powered by machine learning technology, has affected many aspects of our lives. For example, Apple’s Siri uses natural language processing to understand the commands of users and then frame the appropriate responses. Medical image analysis, which uses deep neural networks, is also widely used in the healthcare industry to provide more accurate medical diagnoses. The adoption of machine learning technology in the investment industry has also picked up steam in recent years. Hedge fund managers and other institutional investors are increasingly turning to machine learning for opportunities to generate superior returns. Many have attempted to identify outperforming stocks and estimate the likelihood of bond default, using mathematical algorithms to facilitate their investment decisions. This accelerated adoption is partially driven by an explosive growth in alternative data. Such data is compiled from sources such as financial transactions, sensors, mobile devices, satellites, and the internet, and cannot be handled by traditional software such as Microsoft Excel. Machine learning technology makes it substantially easier to extract information from this hard-to- process data, and this has led to an “arms race” in alternative data among institutional investors. While alternative data has certainly taken the spotlight in recent years, traditional data, particularly data relating to financial statements, also merits a fresh look through the lens of AI. Financial statements, together with other information contained in corporate reports, have long been one of the most important sources of information for investors. The tradition of analyzing financial statements for investment decision making can be traced back at least as far as Graham and Dodd (1934). In their canonical book of value investing, Graham and Dodd devote several hundred pages to analyzing financial statements to arrive at the intrinsic value of a company. Financial Statement Data This “fundamental analysis” has been a dominant approach to investing for almost a century. Traditional fundamental analysis primarily relies on “human intelligence” which requires investors to painstakingly decipher financial statements, together with other information in corporate reports. For example, it is well known that Warren Buffett likes to read corporate filings. He famously got his start as an investor by reading Moody’s manual of publicly listed companies from cover to cover. However, corporate financial reports have become increasingly complex and lengthy. A study finds that the median text length of corporate annual reports in the US doubled from 23,000 words in 1996 to nearly 50,000 words in 2013. 1 Firms also report an intimidating amount of financial statement items. The most commonly used machine-readable financial statement database, COMPUSTAT, currently reports nearly 1,000 items of data for 840,469 firm-year observations of 41,159 unique firms (as of Oct 10, 2021). Complex financial reports impose considerable challenges to investors when processing the information for a large number of firms, and this prevents them from fully appreciating the information content of the financial statements. Indeed, researchers have demonstrated that complex annual reports lead to a significant market underreaction to the information contained in these reports. 2 Thus, it is conceivable that significant information remains hidden in financial statements and is not yet fully understood by investors. Can machine learning technology come to the rescue? The answer is likely to be yes. Machine learning has been developed to efficiently handle high-dimensional data, and is capable of accommodating more complex relationships. As discussed above, financial statements, together with the footnotes, include nearly 1,000 data items. It is a formidable task for a human to process such a large volume of data for thousands of publicly listed companies, but it could be a breeze for a powerful computer armed with advanced AI algorithms. Furthermore, financial statement data is the accumulated result of numerous transactions and it involves a complicated generation process. Rich information is consequently 1 Travis Dyer, Mark Lang, Lorien Stice-Lawrence, 2017, The evolution of 10-K textual disclosure: Evidence from Latent Dirichlet Allocation, Journal of Accounting and Economics, 64(2–3): 221-245. 2 Haifeng You, Xiao-Jun Zhang, 2009, Financial reporting complexity and investor underreaction to 10-K information, Review of Accounting Studies 14: 559-586 3 Kai Cao, Haifeng You, 2021, Fundamental analysis via machine learning: https://papers.ssrn.com/sol3/papers. cfm?abstract_id=3706532 4 Steven J. Monahan, 2018, Financial statement analysis and earnings forecasting. Foundations and Trends® in Accounting 12 (2), 105-215. hidden in the relationships among the line items of different financial statements. These relationships can be very subtle and non-linear in nature, and therefore cannot be easily understood by investors. In contrast, machine learning has the capability to deal with complicated nonlinear relationships, making it a promising technology to navigate through these subtle relationships and extract useful information for investors. Forecasting Corporate Earnings A recent study adopted machine learning technology to perform one of the most important tasks of fundamental analysis, that is, forecasting corporate earnings. This provides clear evidence about the usefulness of the technology in equity investment using financial statement data. 3 Corporate earnings are one of the most important drivers of equity valuation and stock returns. Both academics and industry practitioners have devoted tremendous effort to forecasting earnings accurately. For example, as suggested by Thomson Reuters, over 30,000 analysts from 3,000+ contributing brokers have contributed their earnings forecasts to its IBES database. The revision in these forecasts often triggers dramatic stock price changes. Academics have also developed a battery of statistical models to produce earnings forecasts. However, researchers have challenged the performance of these model-based forecasts, and concluded that they are not much more accurate than the simple guess that future earnings will remain the same as the prior year. 4 The simple approach of combining machine learning and analysts’ forecasts outperforms both individually