這篇文章想記敘一下自己基于周期的思考 ， 之所以不想把之稱為一個 “ 思考框架 ” ， 是不希望 “ 框 ” 和 “ 架 ” 這樣的名詞限制這種思考的延續(xù) ， 同時框架給人一種既成的感覺 ， 然而世界的變化讓思考無法一勞永逸 。 即便是一種框架 ， 亦隨時間和事件的出現(xiàn)不斷自我變更和完善 。這里不用 “ 進化 ” 一詞 ， 系因不同的思考適應不同的市場環(huán)境 ， 不存在一種形態(tài)優(yōu)于另一種形態(tài) — 這種思想本身也是禁錮的 （ 因為禁錮了回溯的可能 ， 而回溯的前態(tài)可能在某一種市場環(huán)境下更有效 ） 。 總之 ， 希望抵達的 ， 是試圖保持靈活 ， 但又提綱挈領 。

我很欣賞卡爾波普爾和他的批判理性主義 ， 這種偏好延續(xù)至今 ， 也影響自己的投資生涯 。 投資是藝術 ， 是科學 ， 而其科學的部分 ， 是人類認知中總體不可降解了的假設(conjecture/hypothesis) ， 是創(chuàng)造性想象出來為解決具體歷史-文化背景下的問題的 ， 因此也不存在一塵不變 。

(資料圖片僅供參考)

所以 ， 邏輯上 ， 沒有經(jīng)驗測試層面上可以實證(positive)確認某科學理論的 ， 但反例的存在可以證偽—在投資中 ， 這種反例在市場中的演繹往往是最出色的投資者的命門 ， 因此個人投資者的認知 ， 必須從動態(tài)的視角不斷完善和迭代 。

01 何為周期

周期的類型有很多種 ， 上面列舉的定義 ， 大多數(shù)投資者理解起來不會有困難 。

寬泛地說 ， 周期就是金融行業(yè)的潮汐 ， 因為資金的涌入和流出而潮起潮落 ， 隨之帶動資產(chǎn)價格的波動 ， 而這種波動 ， 可能產(chǎn)生價格與價值的偏差 ， 讓海灘邊的拾貝人有利可圖 。

從投資者的角度 ， 我感興趣的是流動性與情緒驅動的市場周期 ， 工業(yè)周期 ， 企業(yè)自身的周期 ， 和資本結構定價偏差造成的資產(chǎn)結構套利周期 。 國家層面的大下行周期是一個殺傷范圍很大的風險 ， 但不意味著行業(yè) ， 企業(yè) ， 資本結構 ， 和市場角度沒有機會 。

02 為何周期

每一個投資者的成熟過程而言 ， 本身都是一個不斷尋覓和迭代適合自己的投資范式的過程 ， 各種方法之間無所謂高下 ， 只要能形成一個完備的邏輯體系即可 。

周期對我這樣一個從小愛讀 《 紅樓夢 》 ， 愛背 “ 好了歌 ” 的人來說很直覺 ， 很親切。而我自己的博士項目做的就是商品周期的研究 ， 而且這套方式在過去幾年的重復使用中不斷為我攫取了極高的投資回報 ， 便自然變成了我的工作假設(working hypothesis) ， 直到反例的出現(xiàn) ， 不斷完善該體系 。

我本身是一個喜歡極為廣泛地涉獵各個領域的人 ， 而不喜歡鉆到幾個細分里面去 。 不喜歡深研某一個或幾個行業(yè)給我從前的就業(yè)選擇造成了很大的障礙 （ 因為大多數(shù)基金喜歡招專才 ） ， 但我相信與其自愿地將自己獻祭到某個基金 ， 變成資本社會的一顆螺絲釘 ， 去被剝削 ， 那還不如自己掌握大成的方法 ， 去做那個 “ 剝削者 ” 。 這種 “ 專才 ” 的打法 ， 幾乎限制了從業(yè)者必須把自己框定在某個行業(yè) ， 因此超額收益也必然與該行業(yè)強相關 。

使用 “ 周期 ” 邏輯的好處在于不需要對任何一個行業(yè)有執(zhí)著——事實上 ， 每個我涉獵的大型周期性機會 ， 都是行業(yè)的資深投資人士被悶殺在里面形成的機會 。 這種機會當然需要甄別 ， 最好的就是去聆聽該行業(yè)內資深人士的正反方觀點 ， 然后作為一個沒有倉位的人去做判斷 。

但現(xiàn)在管基金 ， 就沒有辦法使用 ， 畢竟 “ 空倉干等 ” 只是個人投資者的權利 。 這種情況下 ， 采用的修補方式就是廣泛散布倉位到各個行業(yè) ， 預期在沒有斷層/周期性機會的情況下小幅跑贏 ， 甚至小幅跑輸指數(shù) ， 在大斷層情況下 ， 如驚濤駭浪下出現(xiàn)大幅波瀾的浩瀚海洋 ， 去向低洼地做集中突破 ， 一如粟裕在十大戰(zhàn)役中 ， 動態(tài)移動積極尋找戰(zhàn)機 。

另一個使用 “ 周期 ” 的好處 ， 在于其對于資本市場存在必然性 ， 她必定會在某個時點 ， 在某處發(fā)生 ， 無論是哪一種周期 ， 她都是資本市場發(fā)展過程中必然出現(xiàn)的產(chǎn)物 ， 就如同每天早上太陽會升起 ， 每年的春天會回暖一般 。

而大下行周期中 ， 只要對一個行業(yè)有一定的理解 ， 選出好的公司 ， 難度并不大 ， 因這類機會 ， 往往變得顯而易見 。

以下介紹幾個自己親歷過的周期性機會 。

03 行業(yè)與情緒周期

2020年2月11日 ， 由于前一年的暖冬 ， 天然氣價格已經(jīng)回到了1995年的價位 ， 標普全球系統(tǒng)性降低了所有天然氣生產(chǎn)廠家的評級 。

隨之而來的新冠更是重創(chuàng)了該行業(yè) ， 許多能源封閉式基金被迫清盤 。 因此這是一個行業(yè)周期疊加情緒/流動性周期的案例 。 然而事實上 ， 由于頁巖油廠家關井 ， 造成了1/3的天然氣供應 （ 伴生氣 ） 收到阻礙 ， 后來天然氣價格飆升 ， 這些公司的股價也有極好的漲幅 。

當幾個周期疊加共振的時候 ， 其能夠產(chǎn)生的超額回報 ， 可以非常驚人 。 這也是我作為個人投資者的最后/決勝一戰(zhàn) 。

流動性周期

2022年3月 ， 我們知道不少投資中概股的基金都被迫減倉 ， 那種流動性稀缺呈現(xiàn)的機會 ， 也給予了很好的獲利契機 。 由于這個板塊里的標的具有異質性(彼此的行業(yè)重疊度有限 ， 不像天然氣行業(yè)那么均質) ， 因此表現(xiàn)有差異 。

事實上 ， 我在參與這個周期的過程中 ， 也選中了一些 “ 狗 ” ， 比如微博 ， 歡聚時代 ， 但由于拼多多和貝殼的出色表現(xiàn) ， 因此作為一個集合 ， 最后業(yè)績仍非常出色 ， 尤其是在美股大跌的情況下 ， 該板塊為我提供了源源不斷的彈藥 （ 現(xiàn)在基本沒有中概的持倉了 ） 。 這里不對稱的賠率成為了獲利的關鍵 。

不過 ， 當時判斷資金抽離導致競爭減少 ， 變成一個行業(yè)資本周期的預判沒有實現(xiàn) ， 比如年初京東準備開打價格戰(zhàn) ， 國內的幾個龍頭繼續(xù)卷 。 因此這本質上只是一個流動性周期的機會 ， 流動性周期結束 ， 周期邏輯也就結束了 。 當然 ， 如果競爭減緩 ， 股東回報增厚 ， 自然是另一個故事 ， 這也是投資需要動態(tài)評估的部分 。

企業(yè)與資本結構周期

企業(yè)周期 ， 每一個公司都會經(jīng)歷 。 就像人一樣 ， 企業(yè)有上升 ， 有回調 ， 有衰退 ， 也會時不時犯錯 ， 比如優(yōu)等生考了一次不合格 ， 在市場的外推邏輯下被低估 ， 從而出現(xiàn)事件性的買入機會 。

SRG是一家房地產(chǎn)信托公司 。 2021年5月21日 ， 朋友問到這個公司的時候 ， 我看不到流動性好轉的跡象 ， 因此選擇的是不參與 。 但2022年3月 ， 公司明確表達想要探索戰(zhàn)略其他方案(strategic alternative) ， 因此整個邏輯也發(fā)生了變化 — 從自由現(xiàn)金流估值變成了清算價值 。

這個公司值多少錢 ， 很多市場參與者心里是清楚的 。 因此對我而言 ， 這個決策就是一個查理芒格所謂的"cinch" 。 由于這時候我已經(jīng)是投資組合管理者(PM)了 ， 因此在保守原則下 ， 盡管我對SRG的清算價值估值在15美金以上 ， 但我還是采用通過優(yōu)先股的方式參與 ， 因為除了給我10%左右的買入年化分紅與上升空間之外 ， 還因其安全性 ， 給了我重倉的選擇權 。同時 ， 通過比價 ， 我發(fā)現(xiàn)SRG-PA的年化收益當時在75%左右 （ 算上分紅 ） ， 而正股的收益在150%左右 ， 但這個收益預估是高度不確定的 ， 且由于伯克希爾的20億美金的債務形成了巨大的壓迫性杠桿 ， 從風險調整后收益角度SRG-PA的綜合回報率更好 。 事實印證了這一判斷—— 當時市場在不同的資產(chǎn)結構上犯了錯 。

行業(yè)與資本結構周期

2022年以來美聯(lián)儲不斷加息 ， 造成部分銀行的持有到期與待售類權益大幅下跌 ， 包括阿萊恩斯銀行(Western Alliance) 。

但這次周期性的利用 ， 是有瑕疵的 ， 我會分析我的問題 。

3月10日星期五 ， WAL盤中因硅谷銀行被接管的連帶濺射傷害 ， 直接砸到了32美金 。 公司在中午出了公告 ：

這是很簡單的算數(shù) ， 即所有流動性可以覆蓋所有的非FDIC受保存款 。 WAL不存在關門的可能 。 我迅速建立了一個2%左右的頭寸 ， 一個半小時內這個公司的股價都在逡巡 ， 看得我和一名佛州的銀行基金經(jīng)理一頭霧水 ， 后來飛速拉升 ， 到盤尾收在了49美金/股 。 當時我以為自己做了一筆很好的交易 。

周末 ， 簽字銀行(Signature Bank)也被接管 ， 恐慌蔓延 ， WAL盤前下挫到了13美金/股 ， 早上一路跌到了7美金/股 。 我的一名投資人發(fā)給我一個消息 ， 說這個公司被耶倫點名了 。 那時候我已喪失加倉的勇氣 ， 系因什么都不知道 ， 信息上沒有優(yōu)勢 。 公司發(fā)了公告澄清 ， 股價當天翻了四倍 ， 一個早上被我心理清零的公司突然又回來了 。

這次運用周期邏輯的主要還是倉位上的問題 — 因為盡管WAL是一個2%的頭寸 ， 但我還有其它的銀行倉位 ， 因此當持倉板塊遭到重創(chuàng) ， 很難再在沒有信息的優(yōu)勢下大幅集中兵力再戰(zhàn) 。

不過 ， 資本周期層面的機會讓我翻盤 。

既然我知道從流動性角度WAL不會倒 ， 那么其優(yōu)先股 ， 和SRG的案例一樣 ， 就應該是很好的投資機會 。 當優(yōu)先股也從一周前的位置下挫60%+的時候 ， 這就是送錢 。 后來幾天我也假裝客戶多方對WAL的儲蓄開戶情況進行了求證 ， 發(fā)現(xiàn)大量FRC和SVB流出的資金去了WAL那里(因為FDIC可以擔保25萬美金的存款 ） ， 更加讓WAL-PA價格的錯配變得明顯 。 那兩天的天量 ， 讓我可以很快將之變成我們的第一大持倉 。

僅僅一個月出頭 ， 優(yōu)先股就已經(jīng)翻倍了 ， 同期還吃了26美分的分紅(按買入價大概是3%左右的季度紅利) 。

04 周期邏輯的軟肋

周期的本質是均值回歸。 而這種均值回歸的思路 ， 必然會導致三類短板 。 對這些風險和短板的了解 ， 一定程度上也可以消弭他們對這種操作思路造成的不可逆?zhèn)?。

第一，放過沖浪型企業(yè)。很多企業(yè)可以長盛不衰 ， 或者說 ， 其超額收益可以延續(xù)十余年甚至數(shù)十年 ， 而周期的思路會導致抓不住這類企業(yè) ， 造成昂貴的 “ 機會成本 ” 。 對于這一點 ， 我是選擇主動放棄的 。

盡管我聽過很多了不起的抓住長牛的故事 ， 比如李國飛總先是萬科再是騰訊幾百倍的回報 ， 但客觀地說 ， 我現(xiàn)在看到的是他在阿里和平安上的嚴重誤判 。 如果他那樣洞若觀火的投資人都不能集中投資連續(xù)抓住長跑冠軍 ， 我憑什么覺得我可以 ？ 我選擇 “ 守拙 ” ， 用笨辦法 ， 幫我的投資人 ， 賺我看得懂的錢 。

第二，均值不回歸，即林奇所謂的“一片漆黑之前，總是最黑暗的"(It"s always the darkest before pitch black)。利用周期本質是逆向的(contrarian) ， 而集中逆向+自信的錯誤就可能毀掉一個投資人的一生 ， 我們見過很多類似的案例 。

菲利普費雪曾經(jīng)對此說 ， 逆向的時候最好有很高的正確的置信度 ， 而不是為了逆向而逆向 。 汽車出現(xiàn)時馬車股都很便宜 ， 但后來 ， 他們 （ 馬車公司 ） 都消失了 。

因此 ， 逆向之前要非常清楚均值回歸的底層邏輯 ， 比如天然氣未來十年需求還會增長 ， 中國的經(jīng)濟還會增長 ， 美國還會需要銀行體系且該體系目前看不可能被分布式金融(DeFi)顛覆等等 。 沒有辦法比你的對手盤更好地駁斥自己的觀點 ， 就不配擁有這個觀點 。

第三，介入過早/對周期長度的誤判。

我介入能源行業(yè) ， 就早了兩年 。 所幸后來等到了一個超級周期 ， 同時那時本金小不斷有資金補入 ， 因此最后的收益非?？捎^ ， 但未來在基數(shù)很難不斷被補充的情況下 ， 集中性地過早介入 ， 就意味著極大的機會成本 。 就像卡拉曼所說的 ， “ 在這個行業(yè) ， 早就是錯 ” 。

目前我想到的應對方式 ， 尤其是從資管角度 ， 首先是倉位上限的控制 ， 可以給一個更高的集中度 （ 比起市場指數(shù)的比例 ） 但一般不超過市場的3x ， 比如金融行業(yè)占比10% ， 集中度再高也不超過30% ， 沒有什么比活下來更重要 ， 尤其是對于長期主義的選手 ； 其次是行業(yè)的分散和對沖 ， 這對于做多空的選手來說并不難 ， 放棄一部分收益 ， 為了追求更小的風險 ；

最后是對資產(chǎn)結構的運用——一般而言在大下行周期里即便是做資產(chǎn)結構中保守的部分 ， 也可以取得很好的回報 ， 比如Antero Resources的三年到期債券曾經(jīng)賣到過30c/dollar （ 票面價值的30% ） ， SRG-PA和WAL-PA都是很好的例子 （ 目前金融行業(yè)仍有一些不錯性價比的類似資產(chǎn) ） 。

以上一些思考 ， 權作拋磚引玉式的分享 ， 愿對讀者有些許啟發(fā) 。（作者：黑色面包）

附錄：

周期一直是我在研究的一個方向 ， 我也讀了大量的學術領域的文獻 ， 這些文獻給我提供了很好的數(shù)據(jù) ， 邏輯 ， 和統(tǒng)計意義上的支撐 。 雖然和實操的關聯(lián)有限 ， 仍是我感興趣的方向 ， 將一些綜述成果放在附錄 ， 供有需要的讀者參考 ：

Cycles are certain to happen, although less predictable at times. Despite its less of predictability, cycles, in my view, are easier to take advantage of than predicting sustainable growth. Some of the best investment opportunities I have encountered have been cyclical plays, and the best of the best are those with resonating cycles – namely multiple cyclical patterns creating a resonating trough. To better understand cycles is to better prepare oneself for the inevitable, and to better take advantage of such inevitabilities.

Granularity

Table 1: The Granularity of Cycles and the Type of Cycles Involved.

Global Financial Cycles

The importance of the Global Financial Cycle to countries (especially emerging ones) have been argued by scholars. For instance, Rey (2013) argues that “ there is a global financial cycle in capital flows, asset prices, and in credit growth. This cycle co-moves with the VIX, a measure of uncertainty and risk aversion of the markets ” .[1] Passari and Rey (2015) wrote: “ large gross cross-border flows are moving in tandem across countries regardless of the exchange rate regime, they tend to rise in periods of low volatility and risk aversion and decrease in periods of high volatility and risk aversion, as measured by the VIX… There is a global financial cycle ” . [2]

Nevertheless, by analyzing foreign direct investment (FDI), portfolio equity investment, portfolio debt investments, and bank credit as capital flows and VIX, VSTOXX, IVI, and VDAX as proxies for global financial cycles, Cerutti et.al (2017) failed to find a correlation. Without considering endogenous domestic responses to global financial cycles, their results suggest that the global financial cycles explains only a small fraction of the variation in capital flows, then more idiosyncratic phenomena necessarily explain capital flows. [3] My guess is domestic responses partially mitigate such global capital flows, but more research needs to be done to tease out the effect of standalone global financial cycles. In addition, the authors did not investigate the impact of global financial cycles on domestic asset prices or credit, which are critical subjects of capital allocators -- these directions need further analyses as well. Based on existing literature, we know relatively little about the true impact of global financial cycles on domestic.

As a practitioner, based on my personal experience, I believe global capital cycles exist, although they can oftentimes be overwhelmed by country-specific idiosyncratic development. It would be interesting to conduct statistical tests on this subject.

Financial Cycles & Business Cycles

Minsky (1982) and Kindleberger (2000) define financial cycles as the self-reinforcing interactions between asset prices, risk, risk taking, and financing constraints. Some authors go as far as arguing that all recessions in the US since 1985 had financial origins. [4] Financial cycles can be proxied by using bandpass filters with frequencies from 8-32 years to extract medium-term cyclical fluctuations in real (inflation-adjusted) credit, the credit-to-GDP ratio, and real property prices, which are averaged to derive a composite measure of the financial cycle. [5] In addition, another powerful predictor of recession risk is the debt service ratio (DSR), defined as interest payments plus amortization divided by GDP. Drehmann et al (2021) find a strong link between debt accumulation and subsequent debt service, which in turn has a large negative effect on growth. Financial cycles can be described by the joint behavior of leverage and the DSR. [6] More importantly, scholars have found that for a panel of advanced and emerging market economies, financial cycle measures have significant forecasting power both in and out of sample, even for a three-year horizon, outperforming the term spread in nearly all specifications.[7] Business cycles become more fragile when financial booms develop, and typically one financial cycle entails two to three business cycles. However, even this study which employs global panel data focuses only from 1985 on. The authors noted structural differences for the 40 years before 1985 and after, and as practitioners we know a continuous easing of interest rate has been a structural theme for the last 40 years – are we entering a new paradigm with inflation and rates both stepping higher? Will the results produced by this study stand in the new paradigm? How would DSR, financial cycle, and business cycle interact before 1985? These questions remain unanswered by extant literature.

Industrial Cycle and Sentiment Cycle

Scholars have found that firms tend to compete more aggressively in financial distress; the intensified competition in turn reduces profit margins, pushing themselves further into distress and adversely affect other firms. The feedback imposes an additional source of financial distress costs incurred for raising leverage, which helps explain the negative profitability-leverage relation across industries. Owing to the contagion effect, in a decentralized equilibrium, leverage is excessively high from an industry perspective, compromising industry ’ s financial stability.[8] This is particularly true for the shale gas industry pre-pandemic – the producers were levered, and when gas price was under pressure, they produced more natural gas to ride down the cost curve, further depressing gas price, reducing free cashflow, and resulting in greater leverage. This model perfectly describes the down-leg of the last natural gas (and oil) cycle – the mechanism behind the evolution of that industry closely follows Chen ’ s prescription.

Table 2: S&P Ratings, Rating Changes, and Outlook for various natural gas producers at the trough of the last cycle.

However, the model does not address the upcycle. Despite the prevailing contagion which culminated in a near-death experience for the natural gas patch in the US, the producers ultimately dropped enough rigs on a concerted effort, and natural gas price rebounded after Covid hits. In other words, with the contagion reaching a certain degree, from an industrial cycle point of view, the cycle cannot march any lower. Firms start to collaborate tacitly, and the tides start to turn, leading to remarkable upside potential. A more complete model wants to capture the entirety of the industry cycle under a single parsimonious framework.

Picture 1: Performance of public, standalone natural gas producers after they hit the low on Feb 11th, 2020.

Much of the price volatility is also driven by the sentiment cycle, which vastly exaggerates the industrial cycle. This is where Ben Graham believes one can exploit market inefficiencies through mispricing resulted from excessive emotions. Stambaugh et al (2012) find that long-short strategies that exploit the anomalies exhibit profits consistent with this setting. First, each anomaly is stronger (its long-short strategy is more profitable) following high levels of sentiment. Second, the short leg of each strategy is more profitable following high sentiment. Finally, sentiment exhibits no relation to returns on the long legs of the strategies.[9] This confirms Jim Chanos ’ practical observation that historically the alpha on the short side has been as high as 15% in the last two decades, and has been even higher prior to that. Recently, however, he observes that the alpha on the short side has substantially diminished as a result of excessive liquidity by the Federal Reserve. Therefore, it will be interesting to reexamine Stambaugh ’ s work and extend it to consider the interaction between the alpha of the long-short strategy and market liquidity.

Corporate Cycles and Capital Structure Cycles

It has been well documented that there are arbitrage opportunity between a same company ’ s equity and debt given limited liquidity which results in limited arbitrage between equity and credit markets. Short horizon pricing discrepancies across firm ’ s equity and credit markets are common and that an economically significant proportion of these are anomalous, indicating a lack of integration of these two markets. [10] Even for the US market, scholars has found and documented a nontrivial but imperfect integration between its stock and corporate bond markets. [11] Chen et al. proposed a metric named the debt-equity spread defined as the difference between actual credit spread and equity-implied spread. The actual credit spread is calculated from observed bond prices, while the equity-implied credit spread is computed using equity market information through the lens of a standard structural credit risk model. When a firm ’ s equity is valued highly relative to its debt, the equity-implied credit spread tends to be low relative to the actual bond spread, resulting in a higher DES. High-DES firms tend to have more negative growth forecasting revisions (analysts being too optimistic in extrapolation of high growth), are more likely to issue equity and retire debt (Confirming Ma (2019)), and have more insider equity selling. The results are also stronger among smaller, less liquid, and more difficult-to-short stocks and bonds. [12] This could lead to highly interesting opportunities to climb the capital structure ladder for the investors, and thereby selecting the best risk-reward among various types of financial instruments associated with the same company.

Some of the most dangerous yet also most lucrative opportunities exist due to debt overhang, and a deeper understanding of the interaction between macroeconomic risks and agency problems is critical. Scholars have found that firstly, recessions are times of high marginal utilities, which means that the distortions caused by agency problems during such times will affect investors more than in booms; secondly, corporate spreads are strongly countercyclical, thus for a given investment opportunity, the transfer from equity holders to debt holders in a typical procyclical firm tends to concentrate in bad times. [13] In their benchmark case, the debt overhang costs for a low leverage firm peak at less than 0.5% of the total firm value without macroeconomic risk, while these costs peak at 2.7% or 3.6% in booms and recessions, respectively, in the presence of macroeconomic risk. For a high leverage firm, the debt overhang costs peak at 5.1% without macroeconomic risk, while these costs peak at 8.5% or 10.7% in boom and recessions, respectively, with macroeconomic risk. The impact of macroeconomic risk on debt overhang depends on the cyclicality of cash flows from assets-in-place and growth opportunities. More cyclical cash flows from the assets-in-place increase the probability that the firm will underinvest during recessions, when marginal utilities are higher, thus amplifying the impact of macroeconomic risk on the agency cost of debt. The effect of more cyclical cash flows from growth opportunities is ambiguous. On the one hand, more cyclical cash flows from growth opportunities increase the probability that firms will underinvest during recessions. On the other hand, the cost from delaying investment in recessions is lower. In our calibrated model, either of the two effects may dominate. What we can infer from this, is that light capital businesses have lower agency cost of debt.

However, their result is not conclusive. Other researchers have found that compared with firms that are mainly composed of invested assets, firms with growth options have higher costs of debt because they are more volatile and have a greater tendency to default during recession when marginal utility is high and recovery rates are low. Their model matches empirical facts regarding credit spreads, default probabilities, leverage ratios, equity premiums, and investment clustering. Firms with growth options are more likely to default in recessions than those without growth options and thus should have higher credit spreads. [14]

More work is clearly needed to reconcile such contradicting views.

However, what an investor can learn from this is that macroeconomic risks provide high expected return for higher leverage firms that do not default. That is a big “ if ” , which leads to the necessity of understanding the interaction between liquidity, default, and macroeconomic cycles. In addition, an apt metaphor of investing in equity vs. bond is – investing in equity is investing in a call option that shares the upside of the business; investing in bond is shorting a put option on the assets and fundamentals of the business betting the business does not fail. The agency cost issue tells us that equity holders and bond holders are oftentimes pitted against each other with varying incentive structure that should be heeded when we invest in different layers of a company ’ s capital structure.

Longstaff, Mithal, and Neis (2005) calculates liquidity risk by subtracting Credit Default Swap (CDS) swap yield from bond yield, because CDS prices mostly reflects the default risk because of their relatively liquid secondary market. [15] There are two types of interaction terms, namely the “ liquidity-driven default ” and the “ default-driven liquidity ” components, capturing the endogenous positive spiral between default and liquidity. While the latter is easier to understand, the former, namely “ liquidity-driven-default ” , is driven by the rollover risk mechanism in that firms rely on infinite-maturity debt financing will default earlier when facing worsening secondary market liquidity. [16] Chen et al. (2018) found that these interaction terms are quantitatively significant across all ratings. They account for 25-30% of the total credit spread of Aaa/Aa rated bonds and 35-40% of the total spread of Ba rated bonds across the two aggregate states. They account for 27% of the spread increase for Aaa/Aa rated bonds and 55% of the spread increase for Ba rated bonds as the economy switches from a normal state into a recession. [17]

Picture 2: Structural liquidity-default decomposition for 5-year bonds across ratings.

One of the best ways to adapt to various cycles is to pick the right capital allocator at the helm of the company that an investor is interested in. Since the financial crisis of 2008, scholars have found companies cut their investments and payouts in bad times and issues equity in good times even without mediate financing needs, underscoring their salience of the potentiality of financing-window closing as a result of a downward financial cycle. In addition, firms raise capital when their perceived probability of financial conditions worsening. [18] Nevertheless, the aforementioned study along with other studies produce results that challenge recent evidence of the importance of valuation cycles in driving financing waves. In other words, scholars found a positive correlation between equity issuance and stock repurchase waves. [19] An explanation of buy-back at high valuation is that improved financing conditions raise stock prices and lowers the precautionary demand for cash buffers, which in turn can result in more stock repurchases by cash-rich firms. A majority of firms do not seem to allocate capital optimally, and when companies raise cash for the near term, the primary motive is to prevent themselves from running out of cash. Actually, 62.6% of them would run out of cash in a year, and 81.8% have subnormal cash balances. These equity-issuers are primarily not growth firms, and those companies that fail to issue stocks exhibit poor future performance down the road.[20] These studies showcase the importance and the difficulty of becoming partners with great capital allocators given that it seems to be a rare talent.

References:

[1] H. Rey, “ Dilemma not Trilemma: the Global Financial Cycle and Monetary Policy Independence, ” Proceddings 2013 Fed. Reserv. Bank Kansas City Econ. Symp. Jackson Hole, pp. 285–333, 2013.

[2] H. Rey and E. Passari, “ Financial Flows and the International Monetary System, ” Econ. J., vol. 125, pp. 675–698, 2015.

[3] E. Cerutti, S. Claessens, and A. K. Rose, “ How Important is the Global Financial Cycle? Evidence From Capital Flows, ” BIS Work. Pap., 2017.

[4] S. Ng and J. H. Wright, “ Facts and Challenges from the Great Recession for Forecasting and Macroeconomic Modeling, ” J. Econ. Lit., vol. 51, no. 4, pp. 1120–54, 2013.

[5] M. Drehmann, C. Borio, and K. Tsatsaronis, “ Characterising the Financial Cycle: Don ’ t Lose Sight of the Medium Term!, ” BIS Work. Pap., p. 284, 2012.

[6] M. Drehmann, M. Juselius, and A. Korinek, “ Going with the Flows: New Borrowing, Debt Service and the Transmission of Credit Booms, ” SSRN Electron. J., vol. 24549, 2021, doi: 10.2139/ssrn.3168448.

[7] C. Borio, M. Drehmann, and F. D. Xia, “ Forecasting Recessions: the Importance of the Financial Cycle, ” J. Macroecon., vol. 66, 2020.

[8] H. Chen, W. W. Dou, H. Guo, and Y. Ji, “ Feedback and Contagion through Distressed Competition, ” NBER Work. Pap., 2023.

[9] R. F. Stambaugh, J. Yu, and Y. Yuan, “ The Short of It: Investor Sentiment and Anomalies, ” J. financ. econ., vol. 104, no. 2, pp. 288–302, 2012.

[10] N. Kapadia and X. Pu, “ Limited Arbitrage Between Equity and Credit Markets, ” J. financ. econ., vol. 105, no. 3, pp. 542–564, 2012.

[11] M. Sandulescu, F. Trojani, and A. Vedolin, “ Model-Free International Stochastic Discount Factors, ” J. Finance, vol. 76, no. 2, pp. 935–976, 2021.

[12] H. Chen, Z. Chen, and J. Li, “ The Debt-Equity Spread, ” SSRN, 2022.

[13] H. Chen and G. Manso, “ Macroeconomic Risk and Debt Overhang, ” Rev. Corp. Financ. Stud., vol. 6, no. 1, pp. 1–38, 2017.

[14] M. Arnold, A. Wagner, and R. Westermann, “ Growth Options, Macroeconomic Conditions, and the Cross Section of Credit Risk, ” J. financ. econ., vol. 107, no. 2, pp. 350–385, 2013.

[15] F. A. Longstaff, S. Mithal, and E. Neis, “ Corporate Yield Spreads: Default Risk or Liquidity? New Evidence from the Credit Default Swap Market, ” J. Finance, vol. 60, no. 5, pp. 2213–2253, 2005.

[16] Z. He and W. Xiong, “ Dynamic Debt Runs, ” Rev. Financ. Stud., vol. 25, no. 6, pp. 1799–1843, 2012.

[17] H. Chen, R. Cui, Z. He, and K. Milbradt, “ Quantifying Liquidity and Default Risks of Corporate Bonds over the Business Cycle, ” Rev. Financ. Stud., vol. 31, no. 3, pp. 852–897, 2018.

[18] P. Bolton, H. Chen, and N. Wang, “ Market Timing, Investment, and Risk Management, ” J. financ. econ., vol. 109, pp. 40–62, 2013.

[19] A. K. Dittmar and R. F. Dittmar, “ The Timing of Financing Decisions: An Examination of the Correlation in Financing Waves, ” J. financ. econ., vol. 90, no. 1, pp. 59–83, 2008.

[20] H. DeAngelo, L. DeAngelo, and R. Stulz, “ Seasoned Equity Offering, Market Timing, and the Corporate Cycle, ” J. financ. econ., vol. 95, no. 3, pp. 275–295, 2010.

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