SSRN Author: Timothy Falcon CrackTimothy Falcon Crack SSRN Content
https://privwww.ssrn.com/author=32962
https://privwww.ssrn.com/rss/en-usSat, 01 Aug 2020 01:03:38 GMTeditor@ssrn.com (Editor)Sat, 01 Aug 2020 01:03:38 GMTwebmaster@ssrn.com (WebMaster)SSRN RSS Generator 1.0New: Building Modular Dividend Discount Models Using a 'Super Annuity Formula'The dramatic increase in the importance of U.S. dividends since 2001 means that financial analysts may soon demand access to updated dividend discount models (DDMs). To address this need, we introduce a new “super annuity formula” that can be used in the modular construction of multiple-stage level-growth DDMs. We also use the super annuity formula to approximate a three-stage transitional-growth DDM very accurately. We show that these advanced DDMs are relatively robust to the input assumptions. Going forward, every financial analyst should have knowledge of and access to these updated DDMs, so as to capitalize on the growing importance of dividends.
https://privwww.ssrn.com/abstract=3643656
https://privwww.ssrn.com/1927152.htmlFri, 31 Jul 2020 16:59:06 GMTNew: Are Liquid or Illiquid Stocks More Easily Manipulated? The Impact of Manipulator AggressivenessLimit order markets are a common method for trading stocks based on the use of a limit order book to represent buy and sell orders. The issue of market manipulation is a fundamental concern both in terms of market integrity and the ability to detect manipulation cases by market authorities. This paper describes a computational limit order book model where market participants are characterised as liquidity traders, technical traders and manipulators. We simulate this limit order book behaviour and stocks of varying liquidity to determine in which of these stocks a simple pump-and-dump manipulation is easier, more profitable, and more easily detected. Here we show that it is difficult for manipulators to move the price in either very liquid or very illiquid stocks. However, more aggressive manipulators make larger profits in liquid stocks than do less aggressive manipulators, and less aggressive manipulators make more profits in illiquid stocks than do more aggressive manipulators. ...
https://privwww.ssrn.com/abstract=3643650
https://privwww.ssrn.com/1927151.htmlFri, 31 Jul 2020 16:57:43 GMTREVISION: What Everyone Should Know: About Univariate Normality and Bivariate Normality, and How They are Co-Related with Correlation and IndependenceI have collected together 10 results concerning marginal distributions, joint distributions, univariate normality, bivariate normality, correlation and independence. Some of these results are well known, but some are relatively unknown. My experience has been that no single source presents more than a few of these results simultaneously, and whenever I see one or two of these results, I am always left wanting more. <br><br>For example, must uncorrelated normally distributed random variables be independent? (No, I give some counterexamples.) Is independence {\em necessary} for the sum of two normally distributed random variables to be normally distributed? (No, but it is {\em sufficient}.) Is the weaker assumption of bivariate normality {\em necessary} for the sum of two normally distributed random variables to be normally distributed? (No, but it is {\em sufficient}.) If two normally distributed random variables are uncorrelated, are they automatically jointly bivariate normally ...
https://privwww.ssrn.com/abstract=3292639
https://privwww.ssrn.com/1926293.htmlWed, 29 Jul 2020 08:24:22 GMTNew: U.S. Stock Returns, the Berry-Esseen Theorem, and Statistical TestingNeither existing theory nor prior empirical work can tell us the impact of non-normality on required sample sizes for Student-t tests of the mean in U.S. stock returns. Prior empirical work and bounds from a modified Berry-Esseen theorem do suggest, however, that the answer should vary with market capitalization, driven by third moments. For two-tailed nominally 5%-sized one-sample tests, we find that at least 100 observations are needed for large-capitalization stocks, and at least 200 observations are needed for small-capitalization stocks. Larger sample sizes are required for significance levels below 5%, or if one-tailed tests are used with skewed data.
https://privwww.ssrn.com/abstract=3641266
https://privwww.ssrn.com/1925720.htmlMon, 27 Jul 2020 15:27:29 GMTREVISION: What Everyone Should Know: About Univariate Normality and Bivariate Normality, and How They are Co-Related with Correlation and IndependenceI present 10 results regarding marginal distributions, joint distributions, univariate normality, bivariate normality, correlation and independence. For example, can uncorrelated normally distributed random variables fail to be independent? (Yes, I give some counterexamples.) Is independence necessary for the sum of two normally distributed random variables to be normally distributed? (No, but it is sufficient.) Is the weaker assumption of bivariate normality necessary for the sum of two normally distributed random variables to be normally distributed? (No, but it is sufficient.) If two normally distributed random variables are uncorrelated, are they automatically jointly bivariate normally distributed? (No, I give some counterexamples.) Etcetera.
https://privwww.ssrn.com/abstract=3292639
https://privwww.ssrn.com/1915812.htmlWed, 01 Jul 2020 08:42:58 GMTNew: Dual Space Arguments Using Polynomial Roots in the Complex Plane: A Novel Approach to Deriving Key Statistical ResultsWe present a canonical orthogonal decomposition of sample variance and its applications. Surprisingly, our decomposition arises naturally from a novel dual space argument using polynomial roots in the complex plane. Linking these two seemingly disparate literatures yields a new pathway to the derivation of key statistical results under standard assumptions. These results include the chi-squared distribution of the scaled sample variance, the loss of one degree of freedom (relative to sample size) in the sample variance, the distribution of Snedecor’s F-test of differences in dispersion, the independence of the sample mean and sample variance, and the distribution of the one-sample Student-t test of the mean. We suggest several promising directions for future research using our dual space method.
https://privwww.ssrn.com/abstract=3598613
https://privwww.ssrn.com/1906095.htmlMon, 08 Jun 2020 11:02:57 GMTREVISION: Tinkering with Ticks: Choosing Minimum Price Variation for US Equity Markets (1996 Version)For two decades, researchers and practitioners alike have argued that constraining stock prices to eights of a dollar is unnecessarily restrictive. It has repeatedly been suggested that a smaller tick size would narrow bid-ask spreads and increase volume, and that in lower priced stocks the improvement in liquidity would be substantial. These arguments have recently gained both credence and popularity because of rigorous research work (Harris (1991, 1994)), and regulatory endorsement (SEC Market 2000 Report (1994)). However, a natural experiment using a recent change in tick size on the AMEX (from eights to sixteenths) finds little if any benefit to a smaller tick size in low priced stocks. Pervasive clustering of quotes and trades means that, in effect, market participants simply move from trading on eighths to trading on even sixteenths.
https://privwww.ssrn.com/abstract=554962
https://privwww.ssrn.com/1905888.htmlMon, 08 Jun 2020 08:50:09 GMTNew: A Simple Mathematical Model of Mutual Fund Outperformance and Persistence in Terms of Information RatiosI present a simple mathematical model of mutual fund outperformance in terms of the information ratio (IR), that is, a Sharpe ratio in active space. The strength of the model is that it can be used to deduce the likelihood of K-year persistence as a function of IR, either in time series for a single fund, or in pooled data for a group of funds with an assumed distribution of IR. In this model, "K-year persistence" is a mutual fund's outperformance of its benchmark over K years conditional upon its outperformance of the benchmark for the previous K years. <br><br>The model provides an intuitive translation from IR to likelihood of K-year persistence, and vice versa. <br><br>Interesting findings are: a manager with a very good IR can still have a low probability of K-year persistence for high K; a manager with a low (or even negative) IR can have a moderate probability of K-year persistence for low K; and with extreme cross-sectional volatility in IR, both the expected value and the ...
https://privwww.ssrn.com/abstract=3532037
https://privwww.ssrn.com/1873434.htmlSat, 07 Mar 2020 19:30:00 GMTNew: Embedding an NPV Analysis into a Binomial Tree with a Real Options ApplicationFinancial statements and an accompanying NPV calculation are embedded into a binomial tree. This generalization of traditional static NPV analysis allows the financial statements to both evolve through time and, at any given time, to vary with states of the world (similar to a Monte Carlo analysis). Modelling the component cash flows in a tree reveals dynamic detailed structure, leading to a more useful NPV analysis than if only the final cash flow value was modelled in a tree or if component cash flows were modelled without a tree. This dynamic detail provides credible cash flow forecasts that can improve hedging of adverse events and allow for leveraging of beneficial circumstances. The financial statements take the form of pro forma after-tax operating cash flows in this treatment. However, any cash flow model driven by the random variable in the tree and allowing for separate treatment of fixed costs, can be used. The benefits of this technique are illustrated via a real options ...
https://privwww.ssrn.com/abstract=3526901
https://privwww.ssrn.com/1869233.htmlSun, 23 Feb 2020 23:26:03 GMTREVISION: What Quantity Appears on the Vertical Axis of a Normal Distribution? A Student SurveyFinal-year students in statistics, physics, and finance were asked to label the vertical axis on a normal distribution, explain their label, identify units, and answer a question about the impact of x-axis rescaling. Fewer than 4% of the students could both label the vertical axis correctly (as probability density) and explain their label. The most common incorrect vertical axis label was “probability.” Performance on individual survey questions differed significantly by academic discipline, but overall survey performance varied very little, ranging between only 8.8% and 12.5% of survey answers being correct across disciplines. To fill these demonstrated gaps in statistics education, we give counterexamples to demonstrate why “probability” cannot be the correct vertical axis label; we explain why “probability density” is the correct label; and we give an intuitive explanation of probability density and its units. We also indicate how the units of probability density change if the ...
https://privwww.ssrn.com/abstract=3229568
https://privwww.ssrn.com/1863495.htmlTue, 04 Feb 2020 13:45:54 GMTREVISION: What Quantity Appears on the Vertical Axis of a Normal Distribution? A Student SurveyFinal-year students in statistics, physics, and finance were asked to label the vertical axis on a normal distribution, explain their label, identify units, and answer a question about the impact of x-axis rescaling. Fewer than 4% of the students could both label the vertical axis correctly (as probability density) and explain their label. The most common incorrect vertical axis label was “probability.” Performance on individual survey questions differed significantly by academic discipline, but overall survey performance varied very little, ranging between only 8.8% and 12.5% of survey answers being correct across disciplines. To fill these demonstrated gaps in statistics education, we give counterexamples to demonstrate why “probability” cannot be the correct vertical axis label; we explain why “probability density” is the correct label; and we give an intuitive explanation of probability density and its units. We also indicate how the units of probability density change if the ...
https://privwww.ssrn.com/abstract=3229568
https://privwww.ssrn.com/1850272.htmlMon, 16 Dec 2019 09:33:18 GMTNew: A Rookie’s Guide to the Academic Job Market in Finance: The Labor Market for LemonsWe first wrote and circulated our “Rookie’s Guide” paper about the academic labor market for newly minted finance PhDs twenty years ago. It passed hand-to-hand and via photocopies of photocopies sent using snail mail (or, back then, we just called it ‘mail’). Since then, much has changed in our labor market, and much has not. Logistics are easier now due to technological change. But the basic informational frictions that made it difficult for rookies to learn the ropes 20 years ago are still evident, as are the fundamental market frictions that make the bilateral matching between labor demand (hiring schools) and labor supply (the rookies) awkward and challenging. This manuscript refreshes the advice we have given in the past, adds some more specific suggestions for preparing for the intense interview process at the academic meetings, and offers a brief epilogue discussing what a rookie should do at the successful conclusion of the job market. We incorporate by reference some of the ...
https://privwww.ssrn.com/abstract=3433785
https://privwww.ssrn.com/1814455.htmlSat, 10 Aug 2019 20:15:35 GMT