A Fundamental Analysis Approach to Oil and Gas Firm Valuation

Most fundamental analysis studies have focused on fundamentals selected by a data-driven approach on large samples of firms from numerous industries. This paper reports the results of a fundamental analysis of a single industry, the US oil and gas exploration and production industry, using variables identified by industry financial analysts. The results demonstrate a significant relationship between a number of the fundamentals with both the market value of equity and cumulative stock return. The results also suggest that the fundamentals provide incremental information beyond earnings, change in earnings, and book value of equity when explaining equity values and stock returns.     

基于机器学习算法的个体索赔准备金评估模型

非寿险赔款准备金对保险公司的风险管理和财务决策具有重要影响。传统的准备金评估方法通常基于汇总的流量三角形数据进行建模,没有充分利用个体索赔案件的信息,且存在参数过度化、难以处理大额赔款和负增量赔款等问题。本文基于每份保单的个体索赔信息,使用随机森林和XGBoost等机器学习算法对案件的赔付状态、赔付金额分别建立了预测模型,改进了传统准备金评估模型的预测效果。实证研究结果表明,影响赔付状态的因素主要是结案状态、报案延迟等跟案件相关的信息,而影响赔付金额的因素则主要是历史赔付金额等反映出险事故严重程度的信息。本文最后还给出了RBNS准备金的预测分布,其结果更加接近准备金的真实值且方差更小,表明在非寿险RBNS准备金评估中,基于机器学习算法的个体索赔准备金评估模型优于传统的准备金评估模型。     

Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach

We develop a state-of-the-art fraud prediction model using a machine learning approach. We demonstrate the value of combining domain knowledge and machine learning methods in model building. We select our model input based on existing accounting theories, but we differ from prior accounting research by using raw accounting numbers rather than financial ratios. We employ one of the most powerful machine learning methods, ensemble learning, rather than the commonly used method of logistic regression. To assess the performance of fraud prediction models, we introduce a new performance evaluation metric commonly used in ranking problems that is more appropriate for the fraud prediction task. Starting with an identical set of theory-motivated raw accounting numbers, we show that our new fraud prediction model outperforms two benchmark models by a large margin: the Dechow et al. logistic regression model based on financial ratios, and the Cecchini et al. support-vector-machine model with a financial kernel that maps raw accounting numbers into a broader set of ratios.     

债券信用溢价的共同因子与结构变化:基于机器学习方法

共同因子是刻画风险溢价的重要基础,将共同因子模型应用于公司债券市场有助于合理估计信用风险溢价.本文利用机器学习算法探究债券信用溢价因子的存在性以及结构变化后发现:规模、下行风险、价值、波动率以及流动性等五个公司债券共同因子对单个债券信用溢价有较好的解释能力,动量因子对信用溢价的解释能力较差,流动性因子具有较强的逆周期防...     

Thunder Bay Transportation: A Case of Business Valuation and Negotiation*

Thunder Bay Transportation (TBT) is a very versatile case that can be used in several milieus and at various levels of difficulty. The case describes the process involved in the purchase and sale of a private business. Mr. Getzko, owner/manager of TBT, is attempting to sell his business to pursue retirement. Sudbury Systems (SS) is pursuing an acquisition of TBT. Students are required to assume the role of either Mr. Getzko’s accountants (seller) or Sudbury Systems’ accountants (buyer) to: (1) develop a preliminary business value, (2) negotiate adjustments, and (3) arrive at an agreed‐upon business value to finalize the sale. Students will explore both business valuation approaches (earnings based and asset based) and various business valuation concepts (e.g., normalized earnings, sustaining capital reinvestment, earnings multiple, redundant assets, etc.).     

Learning and predictability via technical analysis: Evidence from bitcoin and stocks with hard‐to‐value fundamentals

What predicts returns on assets with “hard‐to‐value” fundamentals such as Bitcoin and stocks in new industries? We are the first to propose an equilibrium model that shows how technical analysis can arise endogenously via rational learning, providing a theoretical foundation for using technical analysis in practice. We document that ratios of prices to their moving averages forecast daily Bitcoin returns in and out of sample. Trading strategies based on these ratios generate an economically significant alpha and Sharpe ratio gains relative to a buy‐and‐hold position. Similar results hold for small‐cap, young‐firm, and low analyst‐coverage stocks as well as NASDAQ stocks during the dotcom era.