Preface

Throughout the economic history of mankind, the “deleveraging” effect caused by financial crisis often leads to drastic fluctuations in the real economy or even serious economic recession. 1996, former Federal Reserve Chairman BS Bernanke put forward the “financial accelerator” theory. This theory laid the foundation of financial economic cycle theory and general analytical framework, for the modern financial economic cycle of many phenomena and features to provide a strong explanatory power.

The financial accelerator theory

BS Bernanke took from the “Great Depression” to explore the non-monetary effects of the “financial accelerator” theory, that is, in the case of information asymmetry, corporate financing depends on the balance sheet position of enterprises (such as the value of collateral). Credit markets tend to be highly isotropic with the real economy. In periods of economic boom and rising house prices, house prices rise, and banks increase their lending supply, accordingly, causing economic overheating; while in times of economic recession, house prices fall and commercial banks reduce their lending, exacerbating the economic depression. This pro-cyclical nature of commercial bank mortgages exacerbated the volatility of the real economy, as evidenced by the Japanese economic crisis in the 1990s and the subprime mortgage crisis in 2008. The “financial accelerator” effect caused by information asymmetry is an important cause of financial crisis. As the information asymmetry of micro and small enterprises is higher, the reliance of loans on collateral is also higher, and this “financial accelerator” effect is especially obvious in the financing of micro and small enterprises.

Financial technology weakens the impact of “financial accelerator”

According to the Bank for International Settlements (BIS) working paper “Data vs collateral”, fintech may reduce the impact of the “financial accelerator” effect. Fintech credit will weaken the correlation between asset prices and credit under the “financial accelerator”. Technology lending uses big data risk control as the basis for risk pricing, analyzing risk in terms of the company’s overall qualifications and operations, rather than relying on collateral. On the one hand, this model can expand the availability of loans and help many micro and small enterprises that cannot provide collateral to obtain loans. On the other hand, it can reduce the reliance on collateral for credit to micro and small enterprises and improve the stability of the financial system. Big tech credit does not correlate with local business conditions and house prices when controlling for demand factors, but reacts strongly to changes in firm characteristics. This implies that a greater use of big tech credit - granted on the basis of machine learning and big data - could reduce the importance of collateral in credit markets and potentially weaken the financial accelerator mechanism.

Financial accelerator in the open economy

Compared with traditional bank credit, fintech credit is less dependent on the financial cycle. In open finance (DeFi), individual credit and assets are completely dependent on crypto assets as collateral, relying on contracts composed of smart contracts to run lending, trading, leverage, clearing and other financial services, which can achieve “trading as settlement” and “trading as clearing “. We believe fa.cash is a social practice in the field of open finance, relying on the decentralized concept of blockchain, and is the world’s first self-growing liquidity system. It is the first in the world to adopt the liquidity self-growing 10 times leveraged revenue aggregation DeFi protocol, the first smart contract-based “leveraged lending” and “leveraged trading," which can perfectly solve the problem of “leverage risk” caused by financial accelerators. Fa.cash, a financial accelerator in the open economy, is the first smart contract-based “leveraged lending” and “leveraged trading” which can perfectly solve the problem of “leverage risk” caused by financial accelerators, while providing users with higher “leverage revenue”.