[MUSIC] Believe it or not at the end of this long, process of the online course end of the day always ends with this sunset. Sunset unfortunately of the investments course, I hope you've had kind of as much fun taking the course as I did kind of putting the material together and then taping it for you, just to kind of think back on the course. Three great modules that kind of enjoyed putting together all providing introduction to kind of risk return models in the world of investments. Talking about efficient markets, applying what we learned in the investments place to firm valuation. So kind of the key goals build up the fundamentals, understand and implement the key asset pricing models, their strengths, their limitations, how they perform in the real world. And we got our kind of rolled up our sleeves and got kind of a little dirty and Excel kind of doing actual regressions based on cap three-factor models. And kind of understand what we can learn, how we can interpret these regression results then at the end introduce firm valuation techniques. So, I think we've accomplished these goals. I hope you feel the same. So, review kind of before we put the official sunset on the course or kind of getting to the dust period now so you better kind of hurry up here. Review of the module first module of the course over the last 80 years. Just what should you know about returns? US stock market has outperformed the US one month treasury bills by about 8% on an annual basis. Small stocks, those in the smallest decile ranking firms by the size of the market value equity. I'll perform the US stock market also by about 8% points on average is past prologue, who knows? But at least it's useful to know the history of returns. One of the things that we could do in the investments course using the development asset pricing models like the CAPM we could then attribute a big portion of the high small stock return relative to the stock market. We contribute a big fraction of that just to small stocks being riskier in the sense of being more sensitive to the state of the economy than the overall US stock market. Other lessons from module 1 wise portfolio formation can reduce risk. Okay, key to putting together an efficient portfolio, a portfolio of risky assets that yields the highest return given the standard deviation key to that is not only the expected returns of the individual assets. But how well the assets work together. If you remember nothing else, you have to remember my hook shot at the begin of module 1. When you're putting a basketball team together, you care about the teamwork. When you're putting investments portfolio together, you care about the teamwork among the securities, the components of the portfolio, how correlated are they. So remember the correlation structure across the individual assets in the portfolio. A key component going into things like the Sharpe ratio, going into things that are like the portfolio standard deviation. Now, when you make an allocation, how much in the risky assets, how much in the risk free asset? Ultimately, that determines on your risk aversion. If you love to take risk more invested in the market, if you don't like to take risk more invested in Treasury bills. And remember avoiding dominated assets. A great example of that is looking at some common mutual fund. Let's see an S&P 500 index funds avoid funds that have the high expense ratios when there's alternatives investing in exactly the same securities with lower expense ratios. So, avoid those dominated assets, module 2 separation theorem. So, kind of very provocative result, looking at developed by kind of Harry Markowitz, investors simply need to focus on separating or allocating their portfolio across two assets. A risk-free asset and then this asset called the Tangency Portfolio, which is a collection of all the risky assets that yields the highest Sharpe ratio. And in equilibrium, this Tangency portfolio turns out to be the market portfolio of risky assets. So, all you need to think about is how much of the portfolio to put in Treasury Bills, how much to put in the kind of market portfolio, kind of a very provocative result. Kind of consistent, like a John Bogle view of the world is like, hey, don't worry about trying to pick winners within a sector or an asset category. Just worry about the asset allocation between bonds and stocks. That would be a notion that's consistent with the separation theorem. More from module 2. Development of the capital asset pricing model, the key insight, the driving force behind the capital asset pricing model is saying returns. Investors won't be compensated in terms of higher returns based on the total volatility of a security. Instead, they should only be compensated for the systematic or market risk of a security. In other words, what determines kind of prices and expected returns? It's the stock, it's the assets BETA. How sensitive is that firms performance to the overall market. If a firm is very sensitive, a stock is very sensitive to the market, the BETA is high. When the market is doing well, this firm does even better. But when the market's doing poorly, this asset, the security kicks you when you're down, that type of security needs to offer you a high return to compensate for this market risk. For the fact that kicks you while your down assets that are giving stable cash flows across the business cycle, they have lower betas are very valuable to investors. Therefore, investors are willing to accept a lower return to hold these. A great example of this is your property insurance. On average, you lose money off a property insurance. The insurance company makes money. Why do you hold the property insurance besides the mortgage company requiring to you hold it because it gives you cash flows in the state of the world when you need it the most, when your house burns down. Same intuition behind the CAPM basically, firms' stocks that have higher sensitivity to the market aren't offering you insurance so they need to offer you higher average returns for you to hold them. Beta measures this sensitivity to the market. Alpha, on the other hand, measures how has your security performed relative to this CAPM benchmark? If the Alpha is positive, you've beat the benchmark if it's negative, you have underperformed the benchmark. Module 3, then looked at given the prominence of the capital asset pricing model used a lot by chief financial officers and a corporate finance setting. A lot of Nobel prizes were awarded in part due to its development: how does it actually perform when you take it to the data? Does the beta predict returns, do higher beta firms like Tiffany's on average have higher returns and lower beta firms like Omar. So there is some weak association in terms of Beta predicting returns on days when macroeconomics is news, the connection between beta and stock returns is stronger. But nonetheless, looking at all the trading days, beta only weakly predicts returns and not as strongly as the capital asset pricing model suggests. Other factors have emerged and predict returns, firm size, value, growth dimension, momentum, market efficiency is the notion that stock prices should reflect all past and present, publicly available information. What's publicly available information? Past stock returns are known, accounting data is known. So these various factors that have been known to predict returns, they're all known, we know firm size, we know if a firm is a value firm or growth firm momentum is a strategy based on past returns. All of these are known today. Okay, we can categorize firms based on these characteristics. So that gives kind of raises a fundamental issue. Are the patterns and returns related to size, value, momentum are they a violation of market efficiency? Okay, so just in general, if there's some factors that's known to predict returns, we need to kind of make a call. Does that predict returns because it represents some type of risk? If it does represent some type of risk, the fact that it can predict returns is not a violation of market efficiency just means riskier assets on average have higher returns or does the factor predict returns. Is there a pattern returns because of some inefficiency in the market? This is a case or hey, there's profits to be made, some investors are screwing up in a predictive away. Let's invest to take advantage of that. Or is the pattern and returns simply due to data mining, remember the example of butter production and Bangladesh being associated or correlated with good US S&P 500 stock market returns. Probably doesn't represent risk or inefficiency in the market just represents data mining, but this is the key issue for looking at return strategies. Active management do return patterns we observe based on size, value momentum. Do they represent exploitation of some inefficiency in the market or do they represent some type of risk? That's the kind of big picture question and asset management. Finally on the module 4 the last of the bunch air take investment finance knowledge, apply it to corporate finance, valuing a firm. We did those two ways market multiples approach or comparables. Using market value data of similar firms, apply that to our financials or accounting data to get an estimate of our market value or the income approach. The textbook discounted cash flow, the value of the firm of the asset is simply the discounted stream of cash flows. It generates, market multiple approach seems easy, but can you really find a comparable company. And even if you do find a comparable company like Yahoo at the time. Yahoo for Google at the time of Google's IPO. You have to sometimes dig deep into the accounting to be sure that you're getting the similar accounting treatment or accounting conventions and how net income is measured kind of differences in book equity. So, even if you find the great comparable company is accounting the same across the firms. And then finally perpetuity valuation looking at a stream of cash flows, we can make some simplifying assumptions of a constant growth rate, constant discount. Then the value today is the simple perpetuity formula, but the key rub here is how comfortable are you in your assumptions for g the growth rate and r the discount rate. And we talked about caution that you should make when you're forecasting, long term growth and long-term hurdle rates for the firm. Well, review a module 4 what's left, goodbye. I know goodbyes can be sad, particularly when they have to be delivered through a video camera like this. This is a very touching painting. This was actually done kind of recreating the scene when I left to kind of go to MIT to get my PhD living leaving from kind of rural Wisconsin. So, I kind of tugs, tugs at the heartstrings, but there's some good news goodbyes. Don't have to be sad in this case are for old times' sake, one last stay tuned. This was a first course on investments representing like the first half of a typical, MBAI investments course. There's going to be a second course on investments that I'm offering topics included in that evaluation of the performance of individual investors, some of the common behavioral factors that go into their decision making. And then plus it's a discussion of tax timing strategies that can help boost your performance in the US we have tax on capital gains, but you only pay the tax when you sell the stock. So, we'll talk about ways that you can take advantage of the US capital gains tax system to help boost your portfolio performance even in a fully efficient market. Also, evaluation of the performance of professional money managers and in particular looking at those that have act strategies where they're trying to predict winners and losers. Are there some pockets of their portfolio that we can identify a priori like maybe their investments in firms headquartered around there. Headquarters, are there some aspects of the portfolio that actually reflects good information? Okay, so that will be kind of our professional money managers and then kind of other topics looking at firm dividend policy decisions, how they affect the value of the firm market reaction to various firm financial decisions. So, more in-depth discussion of how does a market respond to pay out policy decisions, earnings announcements, security issue of the firms. And then talking about the drift that occurs after that post earnings announcement drift. One of the topics will look at and then finally how can options be used to reduce risk or to speculate? So, until then, yeah. Investments to lessons and applications for investors focuses on the composition of returns, capital gains versus dividends, investment decisions and pension plans. The performance of individual investors and the performance of mutual funds and the search for alpha. So, a lot of good stuff. Check it out and remember there will be plenty of animation Scott in the investments to course.