# Critical issues

### + Does DigiFundManager run on Apple?

### + What is the difference between a future prediction and expectations of risks and rewards of investments?

### + Why should I go for CSI as a data provider rather than using free data from the public domain?

### + Why forward looking in an unpredictable future of the stock market?

*DigiFundManager*enables a user to screen and rank stocks based on relatively short-term value fluctuations. The program enables a user to group screened and ranked stocks into portfolios and time and weight those portfolio positions based on his long-term investment objectives. It then enables a user to validate the conformance of his investment plan to his investment objectives over 10 - 55 years. To our knowledge, there is no other software program available to the retail investor that enables this validation.

### + Where does DigiFundManager go beyond Modern Portfolio Theory (MPT)?

*DigiFundManager*.

### + Why managing your own investments of optimal portfolios when a fund manager can do the job?

*DigiFundManager*to self-invest your money, you are able to getting to trade down to each week or multiple of weeks the optimal stock portfolios of Wall Street with expectation values of risk/reward ratios validated over 20+ years that you yourself may be able to get well-balanced of the order of one or even smaller.

### + Do you need 10,000 CPU’s and 90 PhD researchers to configure consistently profitable quantitative investment strategies?

*DigiFundManager*brings the configuration of quantitative strategies to the retail investor for the first time. It enables the retail investor to design his own unique quantitative game plan that fits his own maximum risk endurance and satisfies his own reward expectation.

### + Which model is better on Wall Street: trend-following or mean-reversion?

*DigiFundManager*assumes ergodicity for the statistical behavior of the financial markets. Ergodic behavior assumes that the expected behavior over time equals the expected behavior of an ensemble of stocks traded in the same market at each time. The ensemble of stocks is defined by your Watchlist. Hence, rather than performing the time-consuming processes of analyzing the time-series on trend-following and mean-reversion,

*DigiFundManager*simply ranks the stock in a given Watchlist for a given set of correlation times at each specified time of trading. The ranking model that appears to work the most effective is that the steepest fallers and climbers in a Watchlist have the highest probability to turn around at each specified time.

*DigiFundManager*allows you to base your ranking on two correlation times of your choice. The shortest correlation time is one week and all possible correlation times are a multiple of weeks. Usually, shorter correlation times give higher expected returns and maximum risks. It is not surprising that correlation times of (multiples of) 13 weeks can be found over 30+ years in a Watchlist of the 505 current S&P500 members to produce optimal portfolios of 50 long and 50 short positions with well-balanced risk/reward ratios.

### + How do trades move the stock market?

*DigiFundManager*assumes that all trades are done in perfect competition. That implies that stocks are screened for traded daily-dollar volumes that are large compared to the trade sizes planned by the investor. Trading such stocks should then only negligibly move the market. According to Bloomberg, almost from the beginning, Jim Simons understood that the fund’s overall size can affect its performance: too much money destroys returns. Experts who designed algorithms for quantitative strategies have expressed that researching how your own trades move the market is not an easy challenge. For a retail investor, planned investment amounts will usually leave ample room for large numbers of available stocks with sufficient daily-dollar volumes over the past 30+ years. So you can validate the automated sequential decision-taking process of weekly selecting optimal portfolios of the size of your choice in terms of the risks and expected rewards they produce over those 30+ years. In our opinion, when you keep the solution space linearized, it is not difficult to extend the estimation techniques we apply with a model for a linear feedback mechanism associated with trade sizes. However, in the retail investment market, that extension has little relevance.

### + Is intraday-trading more effective than eod-trading on Wall Street in terms of validated long-term risks and rewards?

*DigiFundManager*only uses simple eod data for its quantitative screening and ranking processes and assumes that investors and traders who choose to apply the program use MOC orders. Its own weekly screened portfolios of top-ranked 250 stocks outperform the 24%/year rewards of the weekly screened portfolios of 238 Zacks’ rank #1 stocks by roughly 10%/year over the 18.4 years prior to April 2018 when we performed this comparison test. Our stocks were screened for minimum daily-dollar volumes of US $0.1million. Zacks uses its own ranking system based on forward-looking estimate revisions of some 3000 analysts at over 150 different brokerage firms. At any given point in time, they are monitoring well over 200,000 earnings estimates and other related data looking for any change. Our weekly screened portfolios of the top-ranked 25 stocks generate 88%/year. It has yet to be seen whether intraday-trading is more effective in terms of risks and rewards than eod-trading.

### + What are the shelf lives or alpha-decay times of classical and quantitative trading strategies?

*DigiFundManager*, quantitative trading software like SmartQuant, QuantShare and DLPAL searches for successful intraday charting patterns over time. It is well known that successful patterns have a finite shelf life and suffer from alpha decay. They can come and go, and come back and go again, but you never know when, only in hindsight. It is believed that the reason rather than cause of this performance decay is the efficiency of the financial markets. One way to measure alpha-decay in the stock market is to measure the t-stat of your investment objective over time. In simple performance terms, this implies that the free cash generated by a sequence of optimal portfolios with constant investments is decaying over time.

*DigiFundManager*is designed to practically instantly reveal such performance decay validated over time spans of 20 – 55 years for each individually configured quantitative investment strategy. The program shows that successful investment strategies of large investments are usually short-lived. However, there is room for smaller investments that accommodate retail investors for already 20+ years. The key to individual success is each individual configuration of our screening, ranking and risk modules in combination with an individual watch list. Broker fees play an important role, but the possibility to go short in any of the entries in your watchlist is even more important. It appears to us from the London-Whale incident that the results of apple orchards shouldn’t be hedged by shorting the results of orange orchards. Short positions intend to take care of the damage control during drawdowns and recessions. Each quarter for already 30+ years, the slowly changing watchlist of the 505 current S&P500 members keeps enabling to configure optimal portfolios of 50 long and 50 short positions with well-balanced risk/reward ratios producing about 12%/year net of broker fees and taxes. The shelf life of this quantitative strategy is already 30+ years as our last demo reveals.

### + How do optimal portfolios perform of investment funds, or of high-EPS, highly-covered, high-dividend, or of S&P500 stocks?

*DigiFundManager*allows you to use any watchlist and screen, rank, time and weight optimal portfolios of the size of your choice from those lists. The screening, ranking, timing, and weighting is done automatically by the program after you configure these processes based on your personal choices and circumstances, including your broker, tax and data provider. All of those resulting game plans are discussed as tutorials in the Manual. Our choices for configuring those game plans usually give risk/reward ratios of roughly 1 – 1.5, except for the one based on investment funds. That latter game plan turns out to be highly risky through the last financial crisis. It has the highest risk and lowest reward. Of the remaining game plans one needs to rebalance the portfolios every week except for the game plan that quarterly picks the optimal portfolios from the current S&P500 members. Except for the last one, you can see so-called alpha decay. Over the past eight years, these three game plans show decreasing performance. As these three game plans require weekly rebalancing, broker fees and dividend tax further significantly exert a downward pressure on the performance. The game plan with highly-covered stocks gives the best performance. The game plan based on current S&P500 members gives the most steady performance without any alpha decay over as many as 30+ years.

### + What are the likelihoods to be successful for an investor account and for a stock to turn their initial values into positive returns?

Probabilities of success for investors’ stock accounts (green curve) and for adjusted share prices of company stocks relative to their IPO’s (red curve) starting on April 2007, the onset of the credit crisis. A minimum daily-dollar volume of US $0.1 is assumed for the stocks.

It is remarkable that the likelihood of success for the companies in terms of their investors value increasing above their IPO’s is more than an order of magnitude larger than the likelihood of success for stock brokerage accounts. The chart shows that since the onset of the credit crisis in April 2007, currently some 11 years ago, the likelihood of success for companies for delivering long-term investor value increasing above their IPO’s is slowly decreasing from 88% to 75%.