The goal of this article is to provide an in-depth understanding of Libertify crypto seatbelt feature.
With the crypto seatbelt, we aim to “reduce the risks associated with crypto investment and securely accompany the investor to build wealth safely.”
We rely on three pillars:
1- A risk scoring engine
The primary goal is to obtain a better knowledge of who the investors are by constantly tailoring the experience to their specific traits. To do this, we create a sophisticated AI with the best interests of the investors in mind.
Customers are subjected to a risk assessment during the onboarding process. That is when their Libertify journey begins. It is their conscious self, or, more accurately, a distorted representation of who they feel they are at that moment.
We believe that realities may alter dependent on market environment as well as other unrelated external variables. Our objective is to adjust our advice depending on the investor’s surrounding environment, which is always changing and is often unique. As a result, we create a custom neural network based on the characteristics of each individual investor, and we keep feeding it with data points produced from their Libertify activities.
We look at the following type of events:
· Investor’s portfolio content
· Investor’s past trades
· Portfolio composition
· Tokens characteristics
· Market context (macro and micro)
· Market sentiments
· Investor’s interactions with Libertify advice
· Lookalike / clustering methods
An investor’s risk score is likely to fluctuate on a regular basis as a consequence of their behavior on Libertify. An investor-specific neural network generates this score change. The Libertify financial algorithm uses this score to figure out how much of each of the investor's assets to put on the market each day.
2- Financial market algorithm
We modeled our recommendation algorithm using three timeless evidence and methodologies that have worked across all asset classes and for more than two thousand years of trading assets.
· Trend following
· Momentum
· Mean reversing — Contrarian strategy
We believe that markets are far from efficient and that prices represent the current reality. Randomness exists because there are a lot of moving parts and a lot of people taking part.
In theory, you could use math to figure out how much all of the parts add up to, but since the number of pieces is infinite, you can't be sure what the price will be in the future.
From this idea, we prefer the probabilistic method for figuring out how likely it is that the next event will happen within a day, which is a good timeframe for small investors.
The market is unpredictable and driven by at least two macro forces:
(1) Rational expectations and (2) Reflexivity (theorized by George Soros), which is not only based on fact (fundamentals), but also on the perception of reality.
This financial market dynamic worsens when leverage and cheap credit come into play. This is because leverage and cheap credit make price action bigger, creating a new reality that changes and affects how market participants see things.
The passage of time produces Boom-Bust cycles that are prevalent in cryptocurrency markets. Trend following is an effective method for harnessing the Boom cycle, which takes time to develop. Trend following is not a kind of forecasting, passive index investing, buy-and-hold, or fundamental research. It uses heuristics, or explicit rules, to benefit from a behavioral perspective. Following trends is straightforward, uncomplicated, and evidence based.
Boom-Bust cycles are established on longer timeframes, although analogous swings on a shorter timescale may be examined as a mean reversing pattern.
Mean reversal capitalizes on large price movements assuming they would return to their original state. It is mostly a statistical market occurrence.
Libertify uses a financial algorithm to look at each asset and then turns the resulting signals into investor-specific recommendations that take risk into account. With the trend-following strategy, our method is based on probabilities. It is stochastic when it comes to measuring the strength of price action, and it is opportunistic when it comes to taking advantage of bounces and letting the market breathe with its mean-reversing technique.
3- An A.I. as the nudging engine
The main part is the "nudging engine," which gets investors to act by getting rid of cognitive biases that lead to inaction and bad economic decisions. Investors are subject to inertia. They'd rather keep doing what they're already doing; unless they're pushed hard to change, they stick with the default choice. Inertia also has to do with our beliefs. We tend to be resistant to changing the way we think. Due to inertia or status quo bias, investors delay making a move (to preserve their portfolio or catch a recovery) even if they know they should.
Today, investors have access to a lot of real-time market data and can get analysis from journalists, experts, and people with a lot of influence. Therefore, a lack of data does not prevent decision-making.
Noise is the primary element stopping investors from making the proper decision. How can an investor who isn't a professional decide what to do when there are so many different, well-supported opinions that may come from different points of view? Or a different risk profile?
Only a mechanical solution, such as an artificial intelligence, can eliminate noise and provide objective and probabilistic advice in a disciplined way, without any desire to manipulate the data.
People's subjective opinions and the noise around them are the two main reasons why their decisions don't do as well as simple, consistent rules that are statistically likely and objectively probable.
Libertify develops its own Natural Language Processing (NLP) model that leverages linguistics and computer science to make human language intelligible to machines. With natural language processing (NLP), Libertify processes pertinent information in a matter of seconds by machine learning and artificial intelligence to automatically examine enormous unstructured data sets.
Hence, writing recommendations can be automated, translating data into precise and actionable language. This makes generating complicated financial advice quicker while maintaining accuracy and consistency.
We are currently compiling a collection of expressions that precisely convey the interpretation of quantitative and technical information derived from various financial indicators.
The solution does not involve adopting a language unique to Libertify, which interprets financial signals. Indeed, our solution considers investors' psychology and risk profile.
The artificial intelligence uses a vocabulary of emotions and a variety of adjectives to add to the richness of the phrases and make the nudges even more specific to the customer's profile.
Using a reinforcement learning method, a loopback mechanism improves the AI by figuring out how often an investor follows a recommendation.
Libertify develops and implements a reinforcement learning system for rewarding positive behaviors (accepting advice) and penalizing undesirable ones (failing to follow the advice when ignoring or dismissing suggestions). This strategy provides positive values to desirable behaviors to encourage investors and negative values to undesirable ones. This instructs the investor to seek the greatest possible long-term return to find the ideal answer.
Conclusion
This article popularizes Libertify’s framework for reducing the cognitive biases that all investors are vulnerable to. Along with a lack of discipline, these biases are one of the main reasons why most investors don't do well with volatile financial assets like stocks and the cryptocurrency market.
After suffering a substantial financial loss, the investor chooses the Buy-and-Hold investment strategy out of spite. Nevertheless, Buy-and-Hold is a suboptimal investing strategy since it considers market losses the same as market rises, leaving the investor completely exposed.
Libertify is a simple solution for all investors that considers human psychology, market volatility, and the discipline that enables over time to outperform the performance of Buy&Hold while drastically decreasing its volatility and falls that may cause investors to panic.
