News from around the world
AltFi published an article examining the question of whether or not the marketplace lending sector in the U.S. has recovered. The writer looks at recent upheavals in the industry exhibited by layoffs and a few bankruptcies. Despite these problems P2P lending remains on solid footing and has, in fact improved amid reports that “online lending equity investment was $2.3 billion in the USA in 2016. Through August 3rd of this year the total stood at $2.5 billion.”
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Forbes released an informative piece covering “Three keys to earning up to 7% with peer-to-peer lending.” The author explains that readers should (1) invest a minimum of $5,000 with diversified holdings, (2) use automated rebalancing tools and finally (3) use P2P investment in conjunction with Tax-deferred accounts.
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City A.M. addressed various myths surrounding P2P lending. The author helped readers understand that stock market downturns have a different impact on P2P investments and might not be as prone to declines in a poor equity market. She also looked at how interest rate changes influence marketplace lending offering that, “interest rates on P2P platforms are not set by the banks, but by the supply of and demand for money.”
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StockInvestor asked “Can investors profit from peer-to-peer lending?” They make a strong case for why it is, in fact, possible to profit from P2P investing because “As a result, P2P lenders are able to provide their services more cheaply than banks and other traditional financial institutions. P2P lenders therefore have the ability to achieve higher returns compared to what might be offered by banks. Borrowers can borrow at reduced interest rates even when a P2P lending company’s fee is included.”
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Inside Trade advocated for P2P investing due to factors like a lower correlation to equities and bonds. Additionally, they explained that diversification is easy with the categorization of borrowers and risk profiles. Finally, they remark on the security of platforms offering that “P2P lending companies use very effective algorithms to determine return rates.”
