Why will Current Exchange Platforms Fail Inevitably?

Let’s break down current systems and figure out why they are doomed to fail at any point in the future. As it usually happens, the answers are not always on the plain sight, so we decided to apply scientific methods to find out what the problems actually are.

Problem #1. Unintended Ponzis.

A Ponzi scheme or Ponzi game is basically a fraudulent investment operation where the operator generates returns for older investors through revenue paid by new investors, rather than from legitimate business activities or profit of financial trading.

The scheme is named after Charles Ponzi, who became notorious for using the technique in the 1920s. The idea was performed in real life by Ponzi, and became well known throughout the United States because of the huge amount of money he took in. Platforms that engage in Ponzi schemes focus their energy into attracting new clients to make investments. Ponzi schemes rely on a constant flow of new investments to continue to provide returns to older investors. When this flow runs out, the scheme falls apart. That is one of the reasons why crypto-exchange platforms collapse — while operating other traders’ assets they heavily rely on constant increase of flow in order to make exchange operations. Once they get a request to exchange a sizable amount of crypto-assets which they cannot accomplish they only have two options:

1. Look for another third-party exchange platform to satisfy the request and lose a seismic amount of money on fees;

2. Collapse without returning the traders’ money and without caring about consequences on the market and on real people’s lives.

Do not be reckless and stop storing your crypto-assets on third-party accounts. By giving them a chance to lose your assets you create a damaging situation both for crypto-market in general and for yourself in particular. With SafeCrypt.io all the assets of the traders remain on their own local wallets and all exchange operations are performed at will allowing the traders to retain absolute control of their assets at all time.

Problem #2. Volatility of the Cryptomarket.

Investors care about volatility of crypto-market mainly for the following reasons:

1. The wider the swings in an investment’s price, the harder emotionally it is to not worry;

2. When certain cash flows from selling a security are needed at a specific future date, higher volatility means a greater chance of a shortfall;

3. Price volatility presents opportunities to buy assets cheaply and sell when overpriced;

4. Disturbing events such as collapses of big exchange platforms directly affect cryptomarket volatility in a negative way and potentially depriving the the investment stategies of the traders of its usefulness.

Note that after the collapse of MtGox at least 850000 BTC were removed from the circulation on the market (all those assets were cynically taken from regular traders’ pockets and never returned).

Within constantly developing crypto-market environment it is unwise for the traders to keep their assets on third-party platforms due to the market’s high volatility index, as a formidable risk factor of losing the crypto-savings.

Problem #3. Lack of Research.

Being a complex system by itself any given exchange platform cannot be viewed and analyzed in isolation from the greater and mathematically by an order of magnitude more complex economic system in its entirety, with a huge number of uncertain input variables and seeming unpredictability of probable outcomes.

Under such conditions it is impossible to model a stably working platform without the implementation of powerful and effective mathematical instruments such as sensitivity analysis.

Sensitivity analysis is the study of how the uncertainty in the output of a mathematical model or system (numerical or otherwise) can be apportioned to different sources of uncertainty in its inputs.

The process of recalculating outcomes under alternative assumptions to determine the impact of a variable under sensitivity analysis can be useful for a range of purposes, including:

1. Testing the robustness of the results of a model or system in the presence of uncertainty.
2. Increased understanding of the relationships between input and output variables in a system or model.
3. Uncertainty reduction, through the identification of model inputs that cause significant uncertainty in the output and should therefore be the focus of attention in order to increase robustness (perhaps, by further research).
4. Searching for errors in the model (by encountering unexpected relationships between inputs and outputs).
5. Model simplification — fixing model inputs that have no effect on the output, or identifying and removing redundant parts of the model structure.
6. Enhancing communication from modelers to decision makers (e.g. by making recommendations more credible, understandable, compelling or persuasive).
7. Finding regions in the space of input factors for which the model output is either maximum or minimum or meets some optimum criterion.
8. In case of calibrating models with large number of parameters, a primary sensitivity test can ease the calibration stage by focusing on the sensitive parameters. Not knowing the sensitivity of parameters can result in time being uselessly spent on non-sensitive variables.
9. To seek to identify important connections between observations, model inputs, and predictions or forecasts, leading to the development of better models.

While designing SafeCrypt system architecture our engineers applied core methodology of sensitivity analysis in order to prevent negative outcomes (e.g. collapse of a platform).

The testing procedures of α-version of SafeCrypt model adhered to the following outline:

a. Quantify the uncertainty in each input (e.g. ranges, probability distributions). Note that this can be difficult and many methods exist to elicit uncertainty distributions from subjective data.

b. Identify the model output to be analysed (the target of interest should ideally have a direct relation to the problem tackled by the model).

c. Run the model a number of times using some design of experiments, dictated by the method of choice and the input uncertainty.

d. Using the resulting model outputs, calculate the sensitivity measures of interest.

Taking into account the complexity of high-dimensional problems of crypto-economic model this procedure had to be repeated multiple times for more precise output data.

Each red dot in the scatter plot represents a collapse of a virtual exchange platform model given that the virtual traders’ assets are stored within the platform. The probability distribution shows an exponential growth of a collapse probability in relation to the amount of crypto-assets stored within the platforms’ virtual crypto-wallets.

The sensitivity analysis output data clearly shows the direct relation of the amount of crypto-assets stored within the virtual models of exchange platforms and the probability of a collapse of the platform model being tested. By applying OFAT approach, time variable (life span of the virtual platform) becomes a secondary parameter, statistically less important for the testing procedure in general but crucial for any particular user — the more assets and the longer they are stored on a given platform by a trader the higher the risks.

By applying sensitivity analysis based on the best practices from the field of multi-criteria decision making (MCDM) studies the problem of how to select the best alternative among a number of competing alternatives becomes a critical task. In such a setting each alternative is described in terms of a set of evaluative criteria. These criteria are associated with weights of importance.

Intuitively, one may think that the larger the weight for a criterion is, the more critical that criterion should be. However, this may not be the case. It is important to distinguish here the notion of criticality with that of importance.

By critical, we mean that a criterion with small change in its weight (amount of assets stored within the given exchange platform) may cause a significant change of the final solution. That is, a sensitivity analysis may shed light into issues not anticipated at the first stages of development.

This, in turn, may dramatically improve the effectiveness of the initial model and assist in the successful implementation of the final solution.

It becomes obvious that storing assets on third-party accounts is adding an unnecessary risk factor to the equation which SafeCrypt.io platform has eliminated from the start. Traders are no longer required to transfer and store their assets elsewhere prior to crypto-exchange operation.

By addressing the question “What’s important to the model development?” SafeCrypt.io engineers managed to identify important connections between observations, model inputs, and mathematical predictions.

It is fair to mention that the results of the the analysis and emulations are striking and by applying the acquired data at the first stages of model designing our developers are building the most stable and scalable risk-free crypto-exchange system which the crypto-market truly demands.

With SafeCrypt.io each crypto-exchange transaction is performed instantly: as soon as the transferred asset is validated by SafeCrypt System, the target asset (requested by trader) is automatically sent to the trader with zero risk and fees as low as possible for the business to remain financially self-sufficient.