**FAQs**

# Pravda Capital

Frequently asked questions

## Expected fund indicators

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**Annual Return**

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**Maximum Drawdown over 20+ years**

### 1. Do you have your own capital invested in the strategy?

Of course we have. We believe that having skin in the game is essential in this industry. Pravda Capital started as a family fund in which we have successfully managed our own money for more than 10 years now. But now we reached a point when we have something exceptional. Until we knew we have something truly valuable, we did not invite people to co-invest. Now, after 10 years of sweat and tears, we do. Now we invite you.

### 2. Do you maximize return at all costs?

No we do not maximize immediate return at all costs. Quite the opposite, we maximize our Sharpe ratio (Sharpe ratio compares return to a risk taken (volatility) of a fund.) over long term while requiring sufficient amount of return at the same time. In contrast, chasing immediate returns through leverage or frantic trading end in tears, research and data show.

### 3. What exactly is a quantitative fund?

A quantitative fund is one that makes investment decisions based solely on statistical and mathematical model run on computers. Strategy changes occur strictly through review of such models.

### 4. Is this high frequency trading?

Not at all, we turn the portfolio around in approximately one year. The intensity of our work is not in trading, but in analyzing, we analyze a lot, and trade as little as possible. We are the opposite of high frequency trading, we are low frequency investing.

### 5. Why should a quantitative fund perform better than an expert trader, broker or broker’s system?

We see two main reasons for that.
Firstly, as documented in academic literature, people are prone to many biases that have a negative impact on their decision making, including action bias, survivorship bias, narrative fallacy and many more. We encounter such biases in our daily lives, but when we are exposed to them when solving very large and complex numerical tasks, they may become fateful for our net worth. The quantitative approach eliminates this type of human error.
Secondly, the financial system is extremely complex, perhaps as complex as the universe itself. Hence, modern finance resembles cosmology more then accounting or economics. The quantitative approach can find relationships that are invisible to the human eye because of the sheer size of the problem.

### 6. Can you guarantee profit?

As Benjamin Franklin said, only two things are certain in life: death and taxes. We live in an uncertain world and any guarantees are only of a probabilistic character. However, based on our research we can speak about a minimal number of years that are necessary for an investor not to have a negative return. That is why we recommend investing for at least three years.

### 7. Is this the right time to invest in stocks?

The general rule is that the timing of the market is extremely difficult, if not impossible. As stated in Asness et al. (2017), opportunistic market timing is one of the reasons why a return of individual investors accounts (that attempt to time the market) is lower than the return of stock indices (by many % per annum). So, the answer is that you hardly ever know, ex-ante, whether it is a good or bad time to invest. It is better to exploit other irregularities in the market rather than trying to be invested at the right time.

### 8. Are stocks too expensive now?

Even though the relationship between stock market valuations and subsequent returns have been documented in academic literature, exploiting this relationship has proven to be a treacherous task. So we take a different view: in order to deliver more stable performance over the whole market cycle (including market crashes) we construct long-short portfolios. Hence, in the case of a market downturn, short positions boost our performance when compared to the stock index.

### 9. Do you use leverage?

Yes, we do. But with moderation and we have a good reason for that. Sophisticated investors do not look at the expected return in isolation. It is only one side of a coin. The expected return should always be compared to a risk that is undertaken, or in other words uncertainty that is associated with it. While using leverage, the expected return increases, but so does the volatility (statistical measure of the dispersion of returns). And for some low volatility strategies, including ours, small leverage is beneficial and contributes to better overall performance.

### 10. Can your algorithm break down, or go crazy?

It is certainly less probable that our model goes crazy than that a human portfolio manager goes crazy. Our model has predefined boundaries and limitations, a person does not. Our algorithms breaking down cannot cause direct losses since its purpose is to analyze and select approximately thousand equities. The only thing that would happen is that our recommendations would become random yet very diverse long-short portfolio. Our failure can be thought of as an investment into a long short ETF (Exchange Traded Fund). Since, most conventional managers are not better than random selection over long period of time, your worst case is an average index or an average conventional manager.

### 11. What is your alpha?

We do not consider alpha a telling or credible statistic, it speaks about returns but not much about risks, especially when considered only for a few years.

### 12. So what measure do you consider credible?

We use Sharpe ratio and we optimize for it long term. Sharpe ratio is defined as return over volatility (surrogate for risk), hence it captures what the fund provides relative to its risk or cost. We believe that our Sharpe ratio is significantly better (above 1) than that of an average ETF (0.5).

### 13. Are you the first applying algorithms to this space?

No we are not the first one generally, but could be one of the first in this particular style of analysis and focus.

### 14. So who was the first one making real money using algorithms?

Renaissance Technologies Inc. has been applying algorithms for decades but in differently. Here is what they say after producing above 30% per year for decades. “One can predict the course of a comet more easily than one can predict the course of Citigroup’s stock. The attractiveness, of course, is that you can make more money successfully predicting a stock than you can a comet.”
Jim Simons, Renaissance Technologies.

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