Books on Investing & Finance
Layman's Guide to Investing in the S&P 500
Strategies for Investing in the S&P 500
Comparison of U.S. Stock Indices
The Value of Share Buybacks
A formal theory is presented for the valuation of share buybacks. Formulas are given for calculating the equilibrium and effect on shareholder value in different share buyback scenarios. The prevalent belief amongst scholars and practitioners is that share buybacks can substitute for dividend payouts as a way for companies to return capital to shareholders, possibly with a tax benefit to shareholders. This so-called substitution hypothesis is shown both theoretically and empirically to be a fallacy as share buybacks may significantly affect the value to shareholders. This is important because share buybacks are now substantially larger than dividend payouts, possibly as a result of the substitution hypothesis commonly believed to be valid. The presented theory is also applied in several case studies.
Artificial Intelligence for Long-Term Investing
This paper presents the results of using a novel Artificial Intelligence (AI) model for long-term investing. The AI model takes various financial data as input signals and tries to determine an optimal portfolio allocation. In these experiments, the AI model considers the stocks of 40 US companies, as well as the S&P 500 index and US government bonds with one-year maturity. The portfolio is rebalanced annually. Between 1995 and 2015, the equal-weighted rebalancing of these 42 assets outperformed the S&P 500 by 5-6% (percentage points) per year on average. The AI model outperformed the equal-weighted rebalancing by 12-13% (percentage points) per year on average, and the AI model outperformed the S&P 500 by about 18% (percentage points) per year on average. It is uncertain and probably unrealistic that this performance advantage of the AI model will continue in the future, but it seems feasible that some combination of AI models could work reasonably well for long-term investing (aka. low-frequency trading).
Portfolio Optimization and Monte Carlo Simulation
This paper uses Monte Carlo simulation of a simple equity growth model with resampling of historical financial data to estimate the probability distributions of the future equity, earnings and payouts of companies. The simulated equity is then used with the historical P/Book distribution to estimate the probability distributions of the future stock prices. This is done for Coca-Cola, Wal-Mart, McDonald's and the S&P 500 stock-market index. The return distributions are then used to construct optimal portfolios using the "Markowitz" (mean-variance) and "Kelly" (geometric mean) methods. It is shown that variance is an incorrect measure of investment risk so that mean-variance optimal portfolios do not minimize risk as commonly believed. This criticism holds for return distributions in general. Kelly portfolios are correctly optimized for investment risk and long-term gains, but the portfolios are often concentrated in few assets and are therefore sensitive to estimation errors in the return distributions.
Monte Carlo Simulation in Financial Valuation
This paper uses Monte Carlo simulation of a simple equity growth model with resampling of historical financial data to estimate the probability distribution of the future equity, earnings and payouts of companies, which are then used to estimate the probability distribution of the future return on the stock and stock options. The model is used on the S&P 500 stock market index and the Coca-Cola company. The relation between USA government bonds, the S&P 500 index and the Dow Jones Venture Capital index (DJVC) is also studied and it is found that there is no consistent and predictable risk premium between USA government bonds and the S&P 500 and DJVC indices, but there is significant correlation between the monthly returns of the S&P 500 and DJVC indices.